Project cost evaluation method and device, equipment and medium

Abstract

The invention discloses a project cost evaluation method and device, equipment and a medium. The method comprises the steps of determining basic cost according to resource prediction information of a target project; establishing a cost influence factor experience library according to the historical development data of the historical project, and determining a decision data matrix according to the cost influence factor experience library; wherein column elements in the decision data matrix represent cost influence factors, and row elements represent cost parameters generated by the influence of the cost influence factors; determining an influence weight of each cost influence factor according to the decision data matrix through a grey correlation algorithm based on an entropy weight; and determining a target cost evaluation result according to the basic cost and the influence weight of each cost influence factor. According to the technical scheme, the problems that project cost evaluation is high in subjectivity and the evaluation result is not high in accuracy and reliability can be solved, and accurate and reliable data support is provided for project cost management and project decision making.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment, and medium for evaluating project costs. Background Technology

[0002] Since there is an upper limit to the amount of project costs that can be allocated within a certain period of time, how to accurately allocate limited costs to more valuable projects has become the core requirement of cost control, and project cost itself is also a key standard for measuring project value and quality.

[0003] Current project cost assessments often rely on subjective judgment or fuzzy analysis, resulting in significant uncertainties and limitations. The current cost assessment results often fail to accurately match the cost requirements of high-value projects, and are also insufficient to quantify the rationality of cost input choices. This ultimately leads to inaccurate and unreliable project cost assessment results, thus hindering the optimal allocation of project costs. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for project cost assessment, in order to solve the problems of high subjectivity in project cost assessment and low accuracy and reliability of assessment results.

[0005] According to one aspect of the present invention, a method for evaluating project costs is provided, comprising:

[0006] Determine the basic cost based on the resource forecast information of the target project;

[0007] An experience database of cost influencing factors is established based on historical development data of historical projects, and a decision data matrix is ​​determined based on the experience database of cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors.

[0008] The influence weights of each cost-influencing factor are determined based on the decision data matrix using a grey relational algorithm based on entropy weights.

[0009] The target cost assessment result is determined based on the base cost and the weight of the impact of each cost influencing factor.

[0010] According to another aspect of the present invention, a project cost assessment apparatus is provided, comprising:

[0011] The basic cost determination module is used to determine the basic cost based on the resource forecast information of the target project;

[0012] The decision data matrix determination module is used to establish an experience base of cost influencing factors based on historical development data of historical projects, and to determine a decision data matrix based on the experience base of cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors.

[0013] The influence weight determination module is used to determine the influence weight of each cost influencing factor based on the decision data matrix using a grey relational algorithm based on entropy weight.

[0014] The cost assessment module is used to determine the target cost assessment result based on the base cost and the influence weight of each of the cost influencing factors.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the project cost assessment method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the project cost assessment method described in any embodiment of the present invention.

[0020] According to another aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the project cost assessment method described in any embodiment of this application.

[0021] The technical solution of this invention determines the basic cost based on the resource forecast information of the target project, ensuring that the basic cost used for the cost assessment of the target project matches the development resource requirements of the target project, and providing reliable data support for subsequent project cost assessments. By establishing an experience base of cost influencing factors based on historical development data of historical projects, and determining a decision data matrix based on this experience base, cost influencing factors and cost parameters can be orderly correlated, forming a structured data set from previously scattered cost-related data, thus improving the efficiency and accuracy of cost influencing factor analysis. Through an entropy-weighted grey relational algorithm, the influence weights of each cost influencing factor are determined based on the decision data matrix, avoiding biases in weight allocation caused by subjective weight settings, thereby ensuring the objectivity and accuracy of weight determination. Furthermore, the precise quantification of the influence weights of each cost parameter clearly presents the degree of influence of different cost influencing factors on project cost assessment, avoiding cost assessment biases caused by the ambiguity of the importance of each cost influencing factor. Determining the target cost assessment result based on the basic cost and the influence weights of each cost influencing factor avoids the problem of inaccurate assessment results due to ignoring the differences in the degree of influence of each cost influencing factor, thus comprehensively and accurately reflecting the cost situation of the target project. Based on the above technical solutions, the problems of strong subjectivity, incomplete consideration of influencing factors, and unreasonable weight allocation in project cost assessment can be solved, thereby improving the accuracy and reliability of project cost assessment and providing accurate and reliable data support for project cost management and project decision-making.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a project cost assessment method provided according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of another project cost assessment method provided by an embodiment of the present invention;

[0026] Figure 3 This is a flowchart of yet another project cost assessment method provided by an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of a project cost assessment device provided according to an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the project cost assessment method of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "candidate," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 The flowchart illustrates a project cost assessment method provided in this embodiment of the invention. This embodiment is applicable to situations requiring project cost analysis and assessment. The method can be executed by a project cost assessment device, which can be implemented in hardware and / or software and can be configured in any electronic device with network communication capabilities.

[0032] like Figure 1 As shown, the project cost assessment method provided in this embodiment of the invention may include the following process:

[0033] S110. Determine the basic cost based on the resource forecast information of the target project.

[0034] A target project refers to a specific project that requires cost assessment. Resource forecasting information refers to the information on various resources expected to be required during the development of the target project, obtained through quantitative estimation. It can be used to characterize the expected scale of resource input for the target project and the constraints on its implementation. For example, for software project development, resource forecasting information may include total investment over the product lifecycle, software product development time, number of effective lines of source code, and the basic development capabilities of the development organization.

[0035] Base cost refers to the baseline project cost required to implement a target project, determined based on resource forecast information. By determining the base cost based on the target project's resource forecast information, a high correlation between the base cost and the target project's implementation needs can be ensured, avoiding blind assessments detached from the actual needs of the target project and providing a reliable data foundation for subsequent target project cost evaluation.

[0036] As an optional but not limited implementation, the basic cost is determined based on the resource forecast information of the target project, including:

[0037] The environmental impact coefficient is determined based on the resource forecast results;

[0038] The basic cost is predicted based on the environmental impact coefficient using a pre-established cost prediction model.

[0039] The environmental impact coefficient can be a quantitative parameter determined based on resource forecasting results, characterizing the degree to which environmental factors affect the cost consumption level during the development of a target project. A cost forecasting model can be a pre-built model used to predict project costs by incorporating environmental impact factors.

[0040] For example, for software project development, a software development cost analysis model is used as a pre-established cost prediction model. By considering three environmental factors affecting software development—personnel, process, and product—the environmental impact coefficient is determined based on the total investment in the product life cycle, software product development time, number of lines of effective source code, and the basic development capabilities of the development organization, which are included in the resource prediction results. The determined environmental impact coefficient is then substituted into the pre-established cost prediction model for calculation to obtain the basic cost.

[0041] S120. Establish an experience base for cost influencing factors based on historical development data of historical projects, and determine a decision data matrix based on the experience base for cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors.

[0042] Historical projects can refer to similar or related projects that completed their entire development process prior to the target project, resulting in complete development records and cost data. Historical development data can refer to various data generated during the implementation of historical projects that are related to the development of the corresponding historical projects. Historical development data can include cost-influencing factors and cost-related information.

[0043] A cost influencing factor experience base refers to a collection of experiences compiled from historical development data of past projects, identifying all factors that may affect project costs. A decision data matrix refers to a matrix-style data set constructed based on the cost influencing factor experience base, with cost influencing factors as columns and the resulting cost parameters as rows. Cost influencing factors refer to various factors that can affect the level or magnitude of project costs; cost parameters refer to specific indicators related to project costs that can be used to quantify the degree of cost impact. Cost parameters can be pre-set according to the cost control objectives or evaluation dimensions of the target project.

[0044] By analyzing historical development data from past projects, various cost-influencing factors that may affect the target project's cost are identified. These identified cost-influencing factors, along with their related cost data, are integrated and stored to construct a cost-influencing factor experience library. This allows for the accumulation and management of cost-influencing factors. Based on this experience library, a decision data matrix is ​​constructed, with columns representing cost-influencing factors and rows representing cost parameters resulting from these factors. This establishes a clear correlation between cost-influencing factors and their corresponding cost parameters, providing a data foundation for subsequent quantitative analysis of the impact of each cost-influencing factor. Optionally, the cost parameters resulting from the influence of cost-influencing factors in the decision data matrix can be obtained from the cost data related to the corresponding cost-influencing factors in the cost-influencing factor experience library.

[0045] By constructing an experience database of cost influencing factors using historical development data, we can summarize and utilize practical experience from past projects, avoiding omissions or oversimplifications of cost influencing factors and ensuring their comprehensiveness and practicality. By constructing a decision data matrix, we can systematically correlate cost influencing factors with cost parameters, transforming previously scattered cost-related data into a structured dataset. This avoids data dispersion and chaos that could lead to analytical difficulties, improving the efficiency and accuracy of subsequent cost influencing factor analysis and providing reliable data support for accurately quantifying the influence weights of each factor.

[0046] S130. Using the grey relational algorithm based on entropy weight, determine the influence weight of each cost influencing factor according to the decision data matrix.

[0047] Entropy weight refers to the weight calculated based on the principle of information entropy. It can be used to reflect the dispersion and importance of the data itself and has the characteristic of objective value assignment. Grey relational analysis algorithm refers to a multi-factor statistical analysis method used to analyze the degree of correlation between various cost influencing factors. It can effectively handle complex data relationships with incomplete or ambiguous information. Entropy weight-based grey relational analysis algorithm can be a comprehensive algorithm that combines the characteristic of entropy weight to objectively determine weights based on the characteristics of the data itself with the uncertainty and incomplete information processing capabilities of grey relational analysis algorithm. Influence weight refers to the weight value used to quantify the degree of influence of each cost influencing factor on the cost assessment of the target project.

[0048] The gray relational analysis algorithm based on entropy weight is used to analyze the decision data matrix. By analyzing the correlation between each cost influencing factor and each cost parameter in the decision data matrix, the influence weight of each cost influencing factor is determined, so as to achieve accurate quantification of the influence of each cost influencing factor on the cost assessment of the target project.

[0049] Using an entropy-weighted grey relational algorithm to determine the influence weights can avoid biases in weight allocation caused by subjective weight setting, thus ensuring the objectivity and scientific nature of the weight determination. Benefiting from the grey relational algorithm's excellent ability to handle uncertain information, it can effectively cope with situations where project cost influencing factors are complex and some information is unclear, thereby improving the adaptability and accuracy of weight calculation. By accurately quantifying the influence weights of each cost parameter, the degree of influence of different cost influencing factors on project cost assessment can be clearly presented, avoiding cost assessment biases caused by the ambiguity of the importance of each cost influencing factor.

[0050] S140. Determine the target cost assessment result based on the basic cost and the influence weight of each cost influencing factor.

[0051] By integrating the basic cost of the target project and the influence weights of each cost parameter according to the preset integrated calculation logic, a target cost assessment result that can comprehensively and accurately reflect the cost level of the target project can be obtained. For example, the preset integrated calculation logic may refer to multiplying the influence weight of each cost influencing factor with the basic cost to obtain the project cost under the influence of each cost influencing factor; the average value of the project cost under the influence of each cost influencing factor is used as the target cost assessment result.

[0052] By combining the basic cost of the target project with the influence weights of each cost parameter, the target cost assessment result can be obtained. This avoids the one-sidedness of the target cost assessment caused by a single cost influencing factor, and also avoids the problem of inaccurate assessment results due to ignoring the differences in the degree of influence of each cost influencing factor. The target cost assessment result can comprehensively and accurately reflect the cost situation of the target project, which can improve the reliability and accuracy of the project cost assessment result. This provides accurate and reliable data support for subsequent work such as project decision-making, cost management, and resource allocation, and enhances the professionalism and effectiveness of project cost management.

[0053] As an optional but not limited implementation scheme, the target cost assessment result is determined based on the base cost and the influence weights of each cost-influencing factor, including:

[0054] The total impact weight is determined by summing the impact weights of each cost-influencing factor;

[0055] The target cost assessment result is determined by multiplying the base cost and the total impact weight.

[0056] The total impact weight is obtained by summing the impact weights of all cost parameters to quantify the comprehensive impact of all cost factors on the target project cost. The target cost assessment result, which can comprehensively and accurately reflect the cost level of the target project, is obtained by multiplying the basic cost and the total impact weight.

[0057] The technical solution of this invention determines the basic cost based on the resource forecast information of the target project, ensuring that the basic cost used for the cost assessment of the target project matches the development resource requirements of the target project, and providing reliable data support for subsequent project cost assessments. By establishing an experience base of cost influencing factors based on historical development data of historical projects, and determining a decision data matrix based on this experience base, cost influencing factors and cost parameters can be orderly correlated, forming a structured data set from previously scattered cost-related data, thus improving the efficiency and accuracy of cost influencing factor analysis. Through an entropy-weighted grey relational algorithm, the influence weights of each cost influencing factor are determined based on the decision data matrix, avoiding biases in weight allocation caused by subjective weight settings, thereby ensuring the objectivity and accuracy of weight determination. Furthermore, the precise quantification of the influence weights of each cost parameter clearly presents the degree of influence of different cost influencing factors on project cost assessment, avoiding cost assessment biases caused by the ambiguity of the importance of each cost influencing factor. Determining the target cost assessment result based on the basic cost and the influence weights of each cost influencing factor avoids the problem of inaccurate assessment results due to ignoring the differences in the degree of influence of each cost influencing factor, thus comprehensively and accurately reflecting the cost situation of the target project. Based on the above technical solutions, the problems of strong subjectivity, incomplete consideration of influencing factors, and unreasonable weight allocation in project cost assessment can be solved, thereby improving the accuracy and reliability of project cost assessment and providing accurate and reliable data support for project cost management and project decision-making.

[0058] Figure 2 This is a flowchart of another project cost assessment method provided by an embodiment of the present invention. This embodiment further refines the process in the above embodiment of establishing an experience base of cost influencing factors based on historical development data of historical projects and determining a decision data matrix based on the experience base of cost influencing factors.

[0059] like Figure 2 As shown, the project cost assessment method provided in this embodiment of the invention may include the following process:

[0060] S210. Determine the basic cost based on the resource forecast information of the target project.

[0061] S220. Filter historical development data from historical development data of historical projects to match different participants.

[0062] Participants can refer to entities that participate in the entire or some stages of project development. Based on the roles, stages, and tasks performed by each entity in the project development process, various types of participants can be identified.

[0063] Using different participants as the matching dimension, targeted data matching rules are constructed for different participants based on their roles, stages, and tasks in project development. Based on the established data matching rules, historical development data of historical projects are classified and filtered, eliminating data that is not related to the corresponding participants, and retaining historical development data that can reflect the cost correlation information of the corresponding participants in project development. This achieves accurate matching between historical development data and different participants, which can effectively avoid interference from invalid and redundant data in subsequent data processing and analysis.

[0064] S230. Establish a corresponding experience base of cost influencing factors based on the historical development data matched by each participating entity.

[0065] By analyzing the historical development data matched with each participant, we can identify and determine the cost influencing factors that have a potential impact on the cost expenditure of each participant during the project development process. For different participants, we can classify and organize the identified corresponding cost influencing factors to establish a cost influencing factor experience library that corresponds one-to-one with each participant. Each cost influencing factor experience library contains various cost influencing factors related to the corresponding participant, as well as cost data information related to each cost influencing factor.

[0066] S240. Determine the decision data matrix corresponding to each participant based on the experience database of cost influencing factors.

[0067] By analyzing the experience base of cost influencing factors corresponding to each participant, the degree of influence of different cost influencing factors on different cost parameters for each participant is determined. Using cost influencing factors as column elements and the resulting cost parameters as row elements, the degree of influence of the determined cost influencing factors on different cost parameters for each participant is quantified into element values ​​in a matrix, thereby constructing a decision data matrix for each participant. Each element value in the decision data matrix represents the degree of influence of the cost influencing factor in the column on the cost parameter in the row.

[0068] By transforming the information from the experience base of cost influencing factors into a structured decision data matrix, the correlation between each cost influencing factor and different cost dimensions of the project can be presented intuitively, providing clear quantitative data support for project cost assessment.

[0069] For example, software development projects under a business outsourcing model typically involve at least three participants: the business department, the technology department, and the outsourcing company. Historical development data from the past projects is collected from these three participants and filtered to obtain historical development data that matches each participant. By analyzing the historical development data matching each participant, the cost influencing factors for each participant can be determined. Specifically, cost influencing factors for the business department include demand factors, urgency, user scope, usage period, and benefits generated; cost influencing factors for the technology department include management of development and post-development maintenance; and cost influencing factors for the outsourcing company include risk factors, functional requirements, development delays, and technical requirements. The historical development data is categorized and organized according to the cost influencing factors corresponding to each participant, establishing a cost influencing factor experience base that corresponds one-to-one with each participant. Based on the general cost assessment dimensions of software development projects, the cost parameters to be assessed include the number of incidents, impact level, additional personnel, amount involved, and time. Using cost influencing factors as column elements and the cost parameters resulting from the impact as row elements, a decision data matrix is ​​constructed for each participating entity based on historical development data provided in the experience base of cost influencing factors for each participating entity.

[0070] Table 1. The impact of different cost influencing factors on different cost parameters for different business units.

[0071]

[0072] Specifically, for business departments, the degree of influence of different cost influencing factors on different cost parameters can be determined, as shown in Table 1. The degree of influence of different cost influencing factors on different cost parameters shown in Table 1 can be quantified into element values ​​in a matrix according to preset quantification rules, thus constructing a decision data matrix corresponding to the business department. The preset quantification rules are pre-defined mapping relationships between quantified values ​​for different levels of impact. For example, in Table 1, the mapping relationship between the impact level and the quantified value is: large = 1, medium = 0.6, small = 0.1; due to factors such as unclear demand, losses may occur, and the mapping relationship between the impact level and the quantified value for the monetary amount is: many = 200, many = 100, medium = 50, few = 40, very few = 20. Similarly, for the technology department, the impact level of different cost influencing factors on different cost parameters can be determined, as shown in Table 2. For outsourcing companies, the impact level of different cost influencing factors on different cost parameters can be determined, as shown in Table 3.

[0073] Table 2. The degree of influence of different cost influencing factors on different cost parameters for the science and technology sector.

[0074]

[0075] Table 3. The impact of different cost influencing factors on different cost parameters for outsourcing companies.

[0076]

[0077] S250. Using an entropy-weighted grey relational algorithm, determine the influence weights of each cost-influencing factor based on the decision data matrix.

[0078] S260. Determine the target cost assessment result based on the basic cost and the influence weights of each cost influencing factor.

[0079] The technical solution of this invention filters historical development data from historical projects that match different participating entities, establishes a corresponding cost influencing factor experience base based on the historical development data matched by each participating entity, and determines the decision data matrix corresponding to each participating entity based on the cost influencing factor experience base. This allows the use of historical experience to ensure the data foundation of the decision data matrix is ​​true and reliable. At the same time, filtering can effectively avoid interference from invalid and redundant data to subsequent data processing and analysis. The decision data matrix enables cost influencing factors to be orderly correlated with cost parameters, forming a structured data set from the originally scattered cost-related data, thereby improving the efficiency and accuracy of cost influencing factor analysis.

[0080] Figure 3 This is a flowchart of another project cost assessment method provided by an embodiment of the present invention. This embodiment further refines the process in the above embodiment of determining the influence weight of each cost influencing factor based on the decision data matrix using a grey relational algorithm based on entropy weight.

[0081] like Figure 3 As shown, the project cost assessment method provided in this embodiment of the invention may include the following process:

[0082] S310. Determine the basic cost based on the resource forecast information of the target project.

[0083] S320. Establish an experience base for cost influencing factors based on historical development data of historical projects, and determine a decision data matrix based on the experience base for cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors.

[0084] S330. Determine the entropy weight of each cost parameter based on the decision data matrix.

[0085] In this embodiment, the entropy value of each cost parameter is calculated based on the decision data matrix, and the entropy weight of the corresponding cost parameter is determined based on the entropy value of each cost parameter. The entropy value of the cost parameter is used to characterize the uniformity of the distribution of the cost parameter under the influence of different cost factors, and the entropy weight of the cost parameter is used to quantify the relative importance of the cost parameter in the cost assessment of the target project. The entropy value and entropy weight of the cost parameter are negatively correlated.

[0086] Alternatively, the entropy weight of each cost parameter can be determined based on the decision data matrix in the following way:

[0087] Based on the decision data matrix, the single-factor cost impact ratio of each cost parameter under the influence of a single cost factor is determined. In the decision data matrix, the column elements represent each cost factor, and the row elements represent the cost parameters affected by the cost factors. The single-factor cost impact ratio is the proportion of the contribution of the cost parameter under the influence of a single cost factor to the contribution of the cost parameter under the influence of all cost factors.

[0088] Based on the information entropy theory, the entropy value of each cost parameter is determined according to the proportion of the cost influence of multiple single factors corresponding to each cost parameter.

[0089] The entropy values ​​of all cost parameters are normalized to obtain the output entropy of each cost parameter. The output entropy is used to characterize the degree of distinguishability of the relative importance of the cost parameter. The degree of distinguishability of relative importance refers to the clarity with which the importance of a cost parameter can be distinguished from the importance of other cost parameters. The degree of distinguishability of relative importance is positively correlated with the uniformity of the distribution of the value of the cost parameter under the influence of different cost factors.

[0090] Based on the output entropy of each cost parameter, the variability of each cost parameter is determined. The variability is used to characterize the response sensitivity of the cost parameter to the effects of cost influencing factors and its contribution to effective information.

[0091] Based on the variability of each cost parameter, the entropy weight of each cost parameter is determined. The variability of the cost parameter is positively correlated with the entropy weight. The higher the variability of the cost parameter, the greater its weight in the cost assessment of the target project, and the more significant its impact on the cost assessment result of the target project.

[0092] Specifically, for the decision data matrix The influence of n cost-influencing factors on m cost parameters in the target project was quantified. , , Used to characterize the degree of influence of the j-th cost factor on the i-th cost parameter. The larger the absolute value, the more significant the influence of the j-th cost factor on the i-th cost parameter. Based on the decision data matrix, according to the formula... Calculations can yield the single-factor cost impact ratio of the j-th cost factor on the i-th cost parameter, ensuring the comparability of values ​​across different cost parameter dimensions. Based on information entropy theory, according to the formula... By calculation, the entropy value of the i-th cost parameter can be obtained. This can be used to reflect the uniformity of the distribution of values ​​for cost parameter i under the influence of different cost factors. Based on the entropy calculation results, according to the formula... The calculated entropy value After normalization, we obtain the output entropy, which can be used to characterize the distinguishability of the relative importance of the i-th cost parameter, where, This represents the maximum value of the entropy. Based on the output entropy result, according to the formula... The variability of the i-th cost parameter is calculated. The magnitude of the variability is positively correlated with the degree of difference in the value of the cost parameter under the influence of different cost factors; the greater the difference in value, the higher the variability. This allows the corresponding cost parameter to more accurately distinguish the cost characteristics of different projects in project cost assessment, providing a valid basis for improving the targeting and accuracy of cost assessment. Based on the variability calculation results, according to the formula... The entropy weight of the i-th cost parameter is calculated.

[0093] S340. Determine the correlation matrix based on the decision data matrix using the grey relational algorithm.

[0094] Using cost parameters in the decision data matrix as a reference sequence and various cost influencing factors as comparison sequences, the correlation coefficient between each comparison sequence and the reference sequence is calculated sequentially according to the analysis logic of the grey relational analysis algorithm. The correlation coefficients are then integrated to obtain the correlation degree. Finally, the correlation degrees between all cost influencing factors and cost parameters are integrated in matrix form to form a correlation matrix that reflects the relationship between each cost influencing factor and each cost parameter. Each element in the correlation matrix characterizes the degree of correlation between a single cost influencing factor and a single cost parameter, that is, the strength of the correlation between different cost parameters and different cost influencing factors.

[0095] Optionally, using the cost parameters in the decision data matrix as the reference sequence and each cost influencing factor as the comparison sequence, the correlation coefficient between each comparison sequence and the reference sequence is calculated sequentially according to the analysis logic of the grey relational algorithm. Specifically, this includes the following process:

[0096] Based on the decision data matrix, two types of analysis sequences are divided. The first type is the reference sequence, which extracts all elements of each row corresponding to the cost parameter in the matrix to form a reference sequence with that cost parameter as the core. The data in the sequence represents the degree of influence of various cost influencing factors on that cost parameter. The second type is the comparison sequence, which extracts all elements of each column corresponding to the cost influencing factor in the matrix to form a comparison sequence with that cost influencing factor as the core. The data in the sequence represents the degree of influence of that factor on various cost parameters.

[0097] Normalization was performed on all reference and comparison sequences to eliminate computational interference caused by differences in data magnitude between different sequences, so that the data of all sequences are in the same comparable range, thus ensuring the objectivity of subsequent correlation calculation results.

[0098] The absolute value of the sequence difference is calculated by comparing the corresponding values ​​at each position in each set of reference sequences and the corresponding comparison sequences. The difference between the corresponding values ​​in each set is calculated, and the absolute value of the difference is taken. The magnitude of the absolute value of the difference directly reflects the degree of deviation between the two sets of sequences at the corresponding positions. The smaller the deviation, the more similar the correlation trend between the reference sequence and the comparison sequence.

[0099] The two-level extreme values ​​are determined by summarizing the absolute values ​​of the differences between all reference and comparison sequences. The minimum value among all values ​​is selected, which is the minimum difference between the two levels. At the same time, the maximum value among all values ​​is selected, which is the maximum difference between the two levels. The two-level extreme values ​​can be used to define the extreme range of deviation of all sequences, providing a benchmark for the subsequent calculation of correlation coefficients.

[0100] The numerator of the correlation coefficient calculation is obtained by adding the products of the minimum difference between the two levels and the resolution coefficient and the maximum difference between the two levels. The denominator of the correlation coefficient calculation is obtained by adding the absolute value of the difference corresponding to any set of data to the product of the resolution coefficient and the maximum difference between the two levels. Dividing the numerator by the denominator yields the correlation coefficient between the reference and comparison sequences. The resolution coefficient is a preset parameter used to reduce the excessive interference of the maximum difference on the calculation results and improve the distinguishability of the correlation between different sets of data. A larger correlation coefficient indicates a stronger correlation between the corresponding cost influencing factors and cost parameters.

[0101] For example, using a grey relational analysis algorithm, based on the decision data matrix of the business department... Determine the incidence matrix. Specifically, after normalizing U, obtain the matrix. Based on the standardized matrix Z, the ideal solution, i.e., the reference sequence, is extracted. Using P0 as the reference sequence and each row of the standardized matrix Z as the comparison sequence, the correlation coefficient of each cost parameter under each cost influencing factor is calculated using the grey relational analysis algorithm. The discrimination coefficient is set to 0.5. For example, the correlation coefficient of the cost parameter in the first row under the cost influencing factor in the first column is... Similarly, after calculating the correlation coefficient of each cost parameter under each cost influencing factor, a correlation coefficient matrix can be obtained. The mean of the obtained correlation coefficient matrix is ​​taken along the cost parameter dimension to obtain the grey correlation degree between each cost parameter and the ideal solution. ,in Based on the grey relational analysis results, the relational degree values ​​corresponding to each cost influencing factor can be extracted. ,Will The higher the correlation value, the more critical the corresponding cost influencing factor is to the project cost assessment result.

[0102] S350. Determine the influence weight of each cost influencing factor based on the correlation matrix and the entropy weight of each cost parameter.

[0103] By using preset comprehensive calculation rules, a weighted calculation is performed based on the correlation matrix and the entropy weight of each cost influencing factor to obtain an influence weight that can comprehensively reflect the correlation degree of each cost influencing factor and the degree of influence of each cost influencing factor on the software development cost, so as to achieve accurate measurement of the role of each cost influencing factor in the project cost assessment process.

[0104] As an optional but not limited implementation, the influence weights of each cost-influencing factor are determined based on the correlation matrix and the entropy weight of each cost parameter, including:

[0105] Determine the direction parameters of influence for each cost-influencing factor;

[0106] The degree of influence of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter;

[0107] The influence weight of each cost influencing factor is determined by multiplying the influence direction parameter and the influence degree parameter.

[0108] The direction of influence parameter refers to a parameter used to characterize the direction of the effect of cost-influencing factors on changes in project costs, reflecting the positive or negative correlation between cost-influencing factors and project costs. Optionally, based on historical cost data related to each cost-influencing factor, the relationship between each cost-influencing factor and project cost changes is analyzed to determine the direction of influence parameter for each cost-influencing factor.

[0109] According to a preset weighted fusion calculation method, the correlation values ​​corresponding to each cost influencing factor in the correlation matrix are weighted and fused with the entropy weights of the corresponding cost parameters to obtain an influence degree parameter that reflects both the close correlation between cost influencing factors and cost parameters, as well as the importance of the cost parameters themselves. The influence direction parameter and influence degree parameter corresponding to each cost influencing factor are then multiplied, and the result is used as the influence weight of each cost parameter. In this embodiment, the influence weight can comprehensively reflect the direction and intensity of the effect of the cost influencing parameters on the project cost corresponding to the cost parameter.

[0110] As an optional but not limited implementation, the influence degree parameters of each cost influencing factor are determined based on the correlation matrix and the entropy weight of each cost parameter, including:

[0111] The influence degree parameter of the cost influencing factors corresponding to the target column is determined by summing the products of the correlation coefficient of each row corresponding to the target column in the correlation matrix and the entropy weight of the cost parameter corresponding to that row.

[0112] The target column can refer to the column vector in the correlation matrix corresponding to any specific cost influencing factor. For each target column in the correlation matrix, the correlation coefficients of all rows corresponding to the target column are extracted. The correlation coefficient of each row is multiplied by the entropy weight of the cost parameter corresponding to that row. All product results are summed, and the sum is used as the influence degree parameter of the cost influencing factor corresponding to the target column. This allows for the precise quantification of the strength of the cost influencing factor's role in project cost assessment.

[0113] S360. Determine the target cost assessment result based on the basic cost and the influence weight of each cost influencing factor.

[0114] The technical solution of this invention determines the entropy weight of each cost parameter based on the decision data matrix, which can quantify the contribution of each cost parameter to project cost assessment, avoid weight allocation deviations caused by subjective weight setting, and ensure the objectivity of weight determination. Through the grey relational algorithm, an association matrix is ​​determined based on the decision data matrix, which can quantify the degree of association between cost influencing factors and cost parameters, improving the comprehensiveness of the association analysis of cost influencing factors. The influence weight of each cost influencing factor is determined based on the association matrix and the entropy weight of each cost parameter, comprehensively considering the degree of influence of the close association between cost influencing factors and cost parameters, as well as the importance of the cost parameters themselves. This avoids bias caused by excessive reliance on subjective experience or single-dimensional information in weight calculation, and improves the comprehensive rationality of the influence weight of cost influencing factors and its guiding value for project cost assessment.

[0115] Figure 4 This is a schematic diagram of a project cost assessment device provided in an embodiment of the present invention. Figure 4As shown, the project cost assessment device provided in this embodiment of the invention may include:

[0116] The basic cost determination module 410 is used to determine the basic cost based on the resource forecast information of the target project.

[0117] The decision data matrix determination module 420 is used to establish an experience base of cost influencing factors based on historical development data of historical projects, and to determine a decision data matrix based on the experience base of cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors.

[0118] The influence weight determination module 430 is used to determine the influence weight of each cost influencing factor based on the decision data matrix using a grey relational algorithm based on entropy weight.

[0119] The cost assessment module 440 is used to determine the target cost assessment result based on the base cost and the influence weight of each of the cost influencing factors.

[0120] Based on the above embodiments, optionally, the influence weights of each cost influencing factor are determined according to the decision data matrix using a grey relational analysis algorithm based on entropy weights, including:

[0121] The entropy weight of each cost parameter is determined based on the decision data matrix.

[0122] The correlation matrix is ​​determined based on the decision data matrix using the grey relational analysis algorithm.

[0123] The influence weight of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter.

[0124] Based on the above embodiments, optionally, the influence weight of each cost influencing factor is determined according to the correlation matrix and the entropy weight of each cost parameter, including:

[0125] Determine the influence direction parameters of each of the aforementioned cost-influencing factors;

[0126] The influence degree parameter of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter;

[0127] The influence weight of each cost influencing factor is determined by multiplying the influence direction parameter and the influence degree parameter.

[0128] Based on the above embodiments, optionally, the influence degree parameter of each cost influencing factor is determined according to the correlation matrix and the entropy weight of each cost parameter, including:

[0129] The influence degree parameter of the cost influencing factor corresponding to the target column is determined by summing the products of the correlation coefficient of each row corresponding to the target column in the correlation matrix and the entropy weight of the cost parameter corresponding to that row.

[0130] Based on the above embodiments, optionally, an experience base for cost influencing factors is established according to historical development data of historical projects, and a decision data matrix is ​​determined according to the experience base for cost influencing factors, including:

[0131] Filter historical development data from the historical development data of the aforementioned historical projects to match different participating entities;

[0132] Establish a corresponding experience database of cost influencing factors based on the historical development data matched by each participating entity;

[0133] The decision data matrix corresponding to each participating entity is determined based on the experience base of cost influencing factors.

[0134] Based on the above embodiments, optionally, the target cost assessment result is determined according to the base cost and the influence weights of each cost influencing factor, including:

[0135] The total impact weight is determined by summing the impact weights of each of the aforementioned cost-influencing factors;

[0136] The target cost assessment result is determined by multiplying the base cost and the total impact weight.

[0137] Based on the above embodiments, optionally, the base cost is determined according to the resource forecast information of the target project, including:

[0138] The environmental impact coefficient is determined based on the resource prediction results.

[0139] The basic cost is predicted based on the environmental impact coefficient using a pre-established cost prediction model.

[0140] The technical solution of this invention determines the basic cost based on the resource forecast information of the target project, ensuring that the basic cost used for the cost assessment of the target project matches the development resource requirements of the target project, and providing reliable data support for subsequent project cost assessments. By establishing an experience base of cost influencing factors based on historical development data of historical projects, and determining a decision data matrix based on this experience base, cost influencing factors and cost parameters can be orderly correlated, forming a structured data set from previously scattered cost-related data, thus improving the efficiency and accuracy of cost influencing factor analysis. Through an entropy-weighted grey relational algorithm, the influence weights of each cost influencing factor are determined based on the decision data matrix, avoiding biases in weight allocation caused by subjective weight settings, thereby ensuring the objectivity and accuracy of weight determination. Furthermore, the precise quantification of the influence weights of each cost parameter clearly presents the degree of influence of different cost influencing factors on project cost assessment, avoiding cost assessment biases caused by the ambiguity of the importance of each cost influencing factor. Determining the target cost assessment result based on the basic cost and the influence weights of each cost influencing factor avoids the problem of inaccurate assessment results due to ignoring the differences in the degree of influence of each cost influencing factor, thus comprehensively and accurately reflecting the cost situation of the target project. Based on the above technical solutions, the problems of strong subjectivity, incomplete consideration of influencing factors, and unreasonable weight allocation in project cost assessment can be solved, thereby improving the accuracy and reliability of project cost assessment and providing accurate and reliable data support for project cost management and project decision-making.

[0141] The project cost assessment device provided in this embodiment of the invention can execute the project cost assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0142] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals.

[0143] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0144] Figure 5A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0145] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0146] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0147] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods described above, such as project cost assessment methods.

[0148] In some embodiments, the project cost assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the project cost assessment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the project cost assessment method by any other suitable means (e.g., by means of firmware).

[0149] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific reference products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0151] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0152] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0153] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data servers), or computing systems that include switching components (e.g., application servers), or computing systems that include front-end components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such back-end, switching, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0154] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0155] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0156] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a project cost assessment method as provided in any embodiment of this application.

[0157] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0158] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0159] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating project costs, characterized in that, The method includes: Determine the basic cost based on the resource forecast information of the target project; An experience database of cost influencing factors is established based on historical development data of historical projects, and a decision data matrix is ​​determined based on the experience database of cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors. The influence weights of each cost-influencing factor are determined based on the decision data matrix using a grey relational algorithm based on entropy weights. The target cost assessment result is determined based on the basic cost and the influence weight of each of the cost influencing factors.

2. The method according to claim 1, characterized in that, The influence weights of each cost-influencing factor are determined using an entropy-weighted grey relational analysis algorithm based on the decision data matrix, including: The entropy weight of each cost parameter is determined based on the decision data matrix. The correlation matrix is ​​determined based on the decision data matrix using the grey relational analysis algorithm. The influence weight of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter.

3. The method according to claim 2, characterized in that, The influence weight of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter, including: Determine the influence direction parameters of each of the aforementioned cost-influencing factors; The influence degree parameter of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter; The influence weight of each cost influencing factor is determined by multiplying the influence direction parameter and the influence degree parameter.

4. The method according to claim 3, characterized in that, The influence degree parameter of each cost influencing factor is determined based on the correlation matrix and the entropy weight of each cost parameter, including: The influence degree parameter of the cost influencing factor corresponding to the target column is determined by summing the products of the correlation coefficient of each row corresponding to the target column in the correlation matrix and the entropy weight of the cost parameter corresponding to that row.

5. The method according to claim 1, characterized in that, An experience database of cost influencing factors is established based on historical development data of historical projects, and a decision data matrix is ​​determined based on the experience database of cost influencing factors, including: Filter historical development data from the historical development data of the aforementioned historical projects to match different participating entities; Establish a corresponding experience database of cost influencing factors based on the historical development data matched by each participating entity; The decision data matrix corresponding to each participating entity is determined based on the experience base of cost influencing factors.

6. The method according to claim 1, characterized in that, The target cost assessment result is determined based on the aforementioned base cost and the influence weights of each of the aforementioned cost influencing factors, including: The total impact weight is determined by summing the impact weights of each of the aforementioned cost-influencing factors; The target cost assessment result is determined by multiplying the base cost and the total impact weight.

7. The method according to claim 1, characterized in that, The base cost is determined based on the resource forecast information of the target project, including: The environmental impact coefficient is determined based on the resource prediction results. The basic cost is predicted based on the environmental impact coefficient using a pre-established cost prediction model.

8. A device for evaluating project costs, characterized in that, The device includes: The basic cost determination module is used to determine the basic cost based on the resource forecast information of the target project; The decision data matrix determination module is used to establish an experience base of cost influencing factors based on historical development data of historical projects, and to determine a decision data matrix based on the experience base of cost influencing factors; wherein, the column elements in the decision data matrix represent each cost influencing factor, and the row elements represent the cost parameters generated by the cost influencing factors. The influence weight determination module is used to determine the influence weight of each cost influencing factor based on the decision data matrix using a grey relational algorithm based on entropy weight. The cost assessment module is used to determine the target cost assessment result based on the base cost and the influence weight of each of the cost influencing factors.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the project cost assessment method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for evaluating project costs according to any one of claims 1-7.

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