A cost index data platform

Abstract

The present invention discloses a cost index data platform, which relates to the technical field of engineering data management. The platform includes a principal component analysis module, a function model fitting module, a current index acquisition module, a unit price prediction module, and a cost calculation module, which are communicatively connected. The function model fitting module obtains the historical principal component index vector corresponding to each historical project data group by calling the principal component analysis module, and constructs a principal component cost function model corresponding to each historical principal component index vector. The cost calculation module matches the model principal component index vector according to the current principal component index vector, and obtains the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index. The present invention reduces the complexity of cost data processing, ensures the accuracy and reliability of the cost calculation results, and realizes effective management and control of project costs.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering data management, and in particular to a cost index data platform. Background Art

[0002] In traditional construction projects, project cost management often relies on rules of thumb and single-metric calculations. This approach estimates project costs based on the project manager's personal experience and simple analogies with historical data. However, as projects grow in scale and complexity, this traditional estimating method has gradually revealed its limitations. These include insufficient data analysis, insufficient cost estimation accuracy, and difficulty adapting to volatile market conditions and project requirements. These issues not only impact project cost control and decision-making quality, but also increase the likelihood of cost overruns.

[0003] To address the limitations of traditional methods, cost management has gradually introduced advanced technologies in recent years. The most commonly used are statistical analysis methods based on historical data. These methods use techniques such as regression analysis to identify factors and patterns influencing cost from data on previously completed projects. While these techniques offer improvements over traditional methods, they can still lead to inaccurate cost forecasts and overly complex data processing in the complex and ever-changing modern project environment. Summary of the Invention

[0004] In order to solve the problems existing in the above background technology, the present invention adopts the following technical solutions:

[0005] A cost index data platform includes a principal component analysis module, a function model fitting module, a current index acquisition module, a unit price prediction module and a cost calculation module connected in communication;

[0006] A principal component analysis module is used to obtain a standardized indicator matrix based on the project data group, and obtain a principal component indicator vector based on the standardized indicator matrix;

[0007] a function model fitting module configured to obtain a plurality of historical project data groups, obtain a historical principal component index vector corresponding to each historical project data group by calling a principal component analysis module, construct a principal component cost function model corresponding to each historical principal component index vector, and store the principal component cost function model and its corresponding model principal component index vector;

[0008] The current indicator acquisition module is configured to organize the current project data into a current project data group, and obtain the current principal component indicator vector corresponding to the current project data group by calling the principal component analysis module;

[0009] a unit price prediction module configured to obtain a predicted unit price corresponding to each principal component indicator in the current principal component indicator vector;

[0010] The cost calculation module is configured to match the model principal component index vector according to the current principal component index vector, and obtain the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index.

[0011] As a preferred solution, the principal component analysis module includes a preprocessing module, a standardization conversion module, a normalization conversion module, a covariance calculation module and a principal component acquisition module;

[0012] The preprocessing module is used to perform data cleaning and data screening on the project data group to obtain a first project data matrix; the standardization conversion module is used to perform standardization processing on the first project data matrix to obtain a second project data matrix; the normalization conversion module is used to perform normalization processing on the second project data matrix to obtain a third project data matrix; the covariance calculation module is used to obtain the covariance matrix of the third project data matrix; the principal component acquisition module is used to obtain the eigenvectors of the covariance matrix and their corresponding eigenvalues, and select several eigenvectors according to the eigenvalues ​​to construct a principal component index vector.

[0013] As a preferred solution, the principal component index vector is expressed as:

[0014] P=[P1,P2,...,P k ],

[0015]

[0016] Among them, P represents the principal component index vector, P1, P2, ..., P k represents the first to kth principal component index, Z represents the second item data matrix, v j represents the eigenvector of the covariance matrix, λ j Represents the eigenvalue corresponding to the eigenvector, m represents the number of eigenvectors of the covariance matrix; k represents the number of eigenvectors of the characteristic matrix; the characteristic matrix is ​​constructed according to the eigenvectors corresponding to the first k largest eigenvalues ​​of the covariance matrix.

[0017] As a preferred solution, the function model fitting module includes a historical data classification module, a historical principal component acquisition module and a genetic fitting module;

[0018] The historical data classification module is used to obtain historical project data and divide the historical project data into several historical project data groups according to the types of attributes;

[0019] The historical principal component acquisition module is used to obtain the historical principal component index vector corresponding to each historical project data group by calling the principal component analysis module;

[0020] The genetic fitting module is used to construct a principal component cost function model corresponding to each historical principal component index vector based on the historical principal component index vector and the genetic programming algorithm, and store the principal component cost function model and its corresponding model principal component index vector.

[0021] As a preferred solution, the method of constructing a principal component cost function model corresponding to each historical principal component index vector includes the following steps:

[0022] S1. Obtain the historical principal component index vector corresponding to each historical project data group;

[0023] S2. Randomly generate an initial population; each population represents a principal component cost function model;

[0024] S3, evaluating the fitness function value of each population for the historical data set according to the fitness function;

[0025] S4. Determine a candidate population for genetic operation based on the fitness function value and the selection algorithm; perform replication, mutation, transposition and / or recombination operations on the candidate population;

[0026] S5. Repeat steps S3 and S4 until the termination condition is met, and store the best performing principal component cost function model and its corresponding historical principal component index vector.

[0027] As a preferred solution, the fitness function is expressed as:

[0028]

[0029] Among them, E represents the preset fitness function value range, Ai is the actual cost value of the population for the i-th sample of the historical project data group, P i is the cost prediction value of the population for the i-th sample of the historical project data set; n is the number of samples in the historical project data set.

[0030] As a preferred solution, the principal component cost function model adopts a tree structure, and the nodes in the tree structure represent mathematical operators or attribute values ​​corresponding to the principal component indicators; the principal component indicators are obtained through historical principal component indicator vectors, and the attribute values ​​corresponding to the principal component indicators are obtained through historical project data groups.

[0031] As a preferred solution, the unit price prediction module includes a prediction index determination module and a price prediction module;

[0032] The prediction index determination module is used to determine the market pricing index in the current principal component index vector;

[0033] The price prediction module is used to obtain the predicted unit price of each market pricing indicator according to a price prediction model; the price prediction model is based on a machine learning model or a time series analysis model.

[0034] As a preferred solution, the cost calculation module includes a principal component matching module, an attribute value compensation module, a first calculation module and a second calculation module;

[0035] The principal component matching module is used to obtain the model principal component index vector with the highest matching degree according to the current principal component index vector;

[0036] The attribute value compensation module is used to obtain the first attribute value corresponding to each principal component index in the current principal component index vector according to the current project data group;

[0037] The first calculation module is used to compensate the first attribute value according to the predicted unit price corresponding to each principal component index to obtain the second attribute value;

[0038] The second calculation module is used to input the second attribute value corresponding to each principal component index into the principal component cost function model corresponding to the model principal component index vector to obtain the cost calculation result.

[0039] As a preferred solution, the project data group stores a number of project numbers and their corresponding attributes and the standard unit prices corresponding to the attributes through a tree structure; the project data group is obtained through a natural language processing model.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention standardizes and reduces the dimension of data through a principal component analysis module, thereby improving the efficiency of calculation and the accuracy of results; performs group analysis on historical data and constructs a principal component cost function model through a function model fitting module, thereby effectively capturing complex data patterns; obtains the predicted unit price corresponding to each principal component indicator in the current principal component indicator vector through a prediction indicator acquisition module, thereby providing a necessary prediction basis for cost calculation; matches the model principal component indicator vector according to the current principal component indicator vector through a cost calculation module, and then obtains the cost calculation result according to the principal component cost function model corresponding to the model principal component indicator vector and the predicted unit price corresponding to each principal component indicator, thereby reducing the complexity of cost data processing, ensuring the accuracy and reliability of the cost calculation result, and further realizing effective management and control of project cost.

[0042] The embodiment of the present invention utilizes the evolution mechanism of the genetic algorithm to perform replication, mutation, transposition and recombination operations based on fitness evaluation, ensuring that the final model performs well in terms of error minimization and generates a principal component cost function model that conforms to the actual project.

[0043] The embodiment of the present invention introduces the dynamic changes of the market pricing model, inputs the compensated second attribute value into the principal component cost function model to dynamically compensate the output of the model without modifying the original principal component cost function model, so that the cost calculation results output by the principal component cost function model can dynamically respond to changes in market prices and be more in line with the predicted market conditions, thereby improving the robustness and applicability of the model in different market environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A schematic diagram of the structure of the cost index data platform provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the structure of a function model fitting module provided in an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of a process for constructing a principal component cost function model corresponding to each historical principal component index vector provided by an embodiment of the present invention;

[0049] Figure 4 This is a schematic structural diagram of an electronic device provided in this embodiment. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0052] In addition, the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] In traditional construction projects, project cost management often relies on rules of thumb and single-metric calculations. This approach estimates project costs based on the project manager's personal experience and simple analogies with historical data. However, as projects grow in scale and complexity, this traditional estimating method has gradually revealed its limitations. These include insufficient data analysis, insufficient cost estimation accuracy, and difficulty adapting to volatile market conditions and project requirements. These issues not only impact project cost control and decision-making quality, but also increase the likelihood of cost overruns.

[0054] To address the limitations of traditional methods, cost management has gradually introduced advanced technologies in recent years. The most commonly used are statistical analysis methods based on historical data. These methods use techniques such as regression analysis to identify factors and patterns that influence cost from data on previously completed projects. Although these techniques offer improvements over traditional methods, they can still lead to inaccurate cost forecasts and overly complex data processing in the complex and ever-changing modern project environment.

[0055] Example 1

[0056] like Figure 1 As shown, a cost index data platform includes a principal component analysis module, a function model fitting module, a current index acquisition module, a unit price prediction module and a cost calculation module connected in communication;

[0057] A principal component analysis module is used to obtain a standardized indicator matrix based on the project data group, and obtain a principal component indicator vector based on the standardized indicator matrix;

[0058] a function model fitting module configured to obtain a plurality of historical project data groups, obtain a historical principal component index vector corresponding to each historical project data group by calling a principal component analysis module, construct a principal component cost function model corresponding to each historical principal component index vector, and store the principal component cost function model and its corresponding model principal component index vector;

[0059] The current indicator acquisition module is configured to organize the current project data into a current project data group, and obtain the current principal component indicator vector corresponding to the current project data group by calling the principal component analysis module;

[0060] a unit price prediction module configured to obtain a predicted unit price corresponding to each principal component indicator in the current principal component indicator vector;

[0061] The cost calculation module is configured to match the model principal component index vector according to the current principal component index vector, and obtain the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index.

[0062] The present invention standardizes and reduces the dimension of data through a principal component analysis module, thereby improving the efficiency of calculation and the accuracy of results; through a function model fitting module, historical data is grouped and analyzed and a principal component cost function model is constructed, thereby effectively capturing complex data patterns; through a current indicator acquisition module, the current project data calls the principal component analysis module to obtain the current principal component indicator vector corresponding to the current project data group, thereby ensuring that the current project data and historical data have a consistent analysis process and enhancing the standardization of data processing; through a prediction indicator acquisition module, the predicted unit price corresponding to each principal component indicator in the current principal component indicator vector is obtained, thereby providing a necessary prediction basis for cost calculation; through a cost calculation module, the model principal component indicator vector is matched according to the current principal component indicator vector, and then the cost calculation result is obtained according to the principal component cost function model corresponding to the model principal component indicator vector and the predicted unit price corresponding to each principal component indicator, thereby reducing the complexity of cost data processing, ensuring the accuracy and reliability of the cost calculation result, and realizing effective management and control of project costs.

[0063] Specifically, the cost index data platform of the present invention is described in detail through the following content:

[0064] A cost index data platform includes a principal component analysis module, a function model fitting module, a current index acquisition module, a unit price prediction module and a cost calculation module connected in communication;

[0065] A principal component analysis module is used to obtain a standardized indicator matrix based on the project data group, and obtain a principal component indicator vector based on the standardized indicator matrix;

[0066] a function model fitting module configured to obtain a plurality of historical project data groups, obtain a historical principal component index vector corresponding to each historical project data group by calling a principal component analysis module, construct a principal component cost function model corresponding to each historical principal component index vector, and store the principal component cost function model and its corresponding model principal component index vector;

[0067] The current indicator acquisition module is configured to organize the current project data into a current project data group, and obtain the current principal component indicator vector corresponding to the current project data group by calling the principal component analysis module;

[0068] a unit price prediction module configured to obtain a predicted unit price corresponding to each principal component indicator in the current principal component indicator vector;

[0069] The cost calculation module is configured to match the model principal component index vector according to the current principal component index vector, and obtain the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index.

[0070] In the cost index data platform of the present invention, the principal component analysis module is used to process the received project data group into a principal component index vector by applying a standardization method and a principal component analysis method, so that attributes of different scales are comparable, and to reduce the dimensionality of the data in the high-dimensional space and extract the main principal components, thereby obtaining a principal component index vector representing the project data. The function model fitting module classifies historical project data according to attribute types to form several historical project data groups; uses the principal component analysis module to analyze each data group and extracts its historical principal component index vector; then, based on the historical principal component index vector, a principal component cost function model that can map the principal component index vector to the cost is fitted, and finally the fitted principal component cost function model and its corresponding historical principal component index vector are stored for subsequent prediction; the current index acquisition module collects relevant data of the current project and organizes it into a current project data group that meets the requirements, and then extracts the current principal component index vector of the current project data group by calling the principal component analysis module; the unit price prediction module obtains the price prediction of each principal component index in the current project principal component index vector through corresponding market analysis, historical data or external data sources for subsequent cost calculation; the cost calculation module matches the model principal component index vector according to the current principal component index vector, and then obtains the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index.

[0071] Furthermore, the project data set uses a tree structure to store multiple project numbers, their corresponding attributes, and the standard unit prices corresponding to the attributes. When using a tree structure to store the project data set, project numbers can be connected through parent-child node relationships. Specifically, within the project data set, nodes connected by parent-child node relationships have different hierarchical relationships. A large project (parent node) can contain multiple subprojects (child nodes). These subprojects exist as independent work packages but are dependent on the overall goals of the large project.

[0072] Each project number can correspond to multiple attributes, such as construction period, environmental indicators, materials, personnel, equipment and other data. Attributes can also be connected through parent-child node relationships. For example, under the "Materials" category, there can be different materials' names, quantities, standard unit prices and total costs.

[0073] In one embodiment, the project data set is acquired through a natural language processing model. Based on natural language processing (NLP), this embodiment can parse unstructured text data such as project documents, contracts, and bids, storing key data in a structured format. Furthermore, by using semantic analysis and knowledge graph technology, unstructured project data is associated with existing indicators to automatically generate contextually relevant tags and annotations for each project, thereby enabling the storage and management of project data sets in a tree-like structure.

[0074] In this embodiment, the tree structure can help understand and manage the relationship between complex projects, clarify the affiliation between projects, and facilitate the clarification of the hierarchical relationship and attribute distribution of each project in data analysis, thereby optimizing resource allocation and progress tracking.

[0075] Furthermore, the principal component analysis module includes a preprocessing module, a standardization conversion module, a normalization conversion module, a covariance calculation module and a principal component acquisition module;

[0076] The preprocessing module is used to clean and filter the project data set to obtain a first project data matrix. Data cleaning removes noise and outliers from the data through dispersion analysis. Data filtering selects attributes corresponding to the project number and removes irrelevant or redundant data based on predefined criteria. For example, if a project data set includes land area as an attribute, the preprocessing module removes outliers (such as obviously incorrect area data) and noise (such as a temporarily incorrect price entry), and filters out relevant data based on predefined criteria (such as removing duplicate or unnecessary data columns).

[0077] The standardization conversion module is used to standardize the first project data matrix to obtain a second project data matrix. In the project data group, the dimensions of each attribute may be different. The standardization conversion module converts these attributes into a form with a mean of 0 and a standard deviation of 1, so that each attribute can be compared and analyzed on the same scale. The elements of the second project data matrix are represented as follows:

[0078]

[0079] Among them, z ij represents the attribute value corresponding to the jth attribute of the i-th sample in the second project data matrix, x ijrepresents the attribute value corresponding to the jth attribute of the i-th sample in the first item data matrix, μ j represents the attribute average value of the jth attribute in the first item data matrix; σ j Represents the attribute standard deviation value of the j-th attribute in the first item data matrix.

[0080] The normalization conversion module is used to normalize the second project data matrix to obtain a third project data matrix; the normalized data matrix is ​​normalized to convert the data values ​​into a unified range of [0, 1]. The normalized data may still have different magnitudes. The normalization conversion module maps this data to the range of 0 to 1, making subsequent analysis more stable and accurate. The elements of the third project data matrix are represented as follows:

[0081]

[0082] Among them, z ij represents the attribute value corresponding to the jth attribute of the i-th sample in the second project data matrix, z max,j Indicates the maximum value of the attribute corresponding to the jth attribute in the second item data matrix, z min,j Indicates the minimum attribute value corresponding to the j-th attribute in the second item data matrix.

[0083] The covariance calculation module is used to obtain the covariance matrix of the third project data matrix; based on the third project data matrix, a covariance matrix composed of covariances between attributes is obtained to reveal the correlation between the attributes.

[0084] Wherein, the covariance matrix is ​​expressed as:

[0085]

[0086] Where C represents the covariance matrix, m represents the number of samples, R represents the third item data matrix, and T represents the transposed matrix.

[0087] The principal component acquisition module is used to obtain the eigenvectors of the covariance matrix and their corresponding eigenvalues, and select several eigenvectors based on the eigenvalues ​​to construct the principal component index vector. The principal component acquisition module allows us to select the first few principal components (the eigenvectors corresponding to the largest eigenvalues) by analyzing the eigenvalues. The principal components can represent the main influencing factors in the project data set, reducing the dimensions without losing too much data information, thereby simplifying the analysis model. Among them, the eigenvectors of the covariance matrix are obtained using the formula:

[0088] Cv j =λ j v j ,

[0089] Where C represents the covariance matrix, v j represents the eigenvector of the covariance matrix, λ j Represents the eigenvalue corresponding to the eigenvector, j represents the characteristic subscript of the covariance matrix, j = 1, 2, ..., m, m represents the number of eigenvectors of the covariance matrix.

[0090] Based on the eigenvectors and their corresponding eigenvalues ​​obtained in the above steps, the eigenvectors are sorted from large to small according to the eigenvalues, and the characteristic matrix V is constructed according to the eigenvectors corresponding to the first k largest eigenvalues. k , and then transform the third item data matrix into the space of the feature matrix to obtain the principal component indicator vector.

[0091] Among them, the feature matrix V k The characteristic matrix V is composed of the eigenvectors corresponding to the first k largest eigenvalues. k The elements in represent the original features. The principal component index vector is expressed as:

[0092] P=[P1,P2,...,P k ],

[0093]

[0094] Among them, P represents the principal component index vector, P1, P2, ..., P k represents the first to kth principal component index, Z represents the second item data matrix, v j represents the eigenvector of the covariance matrix, λ j Represents the eigenvalue corresponding to the eigenvector, m represents the number of eigenvectors of the covariance matrix; k represents the number of eigenvectors of the characteristic matrix; the characteristic matrix is ​​constructed according to the eigenvectors corresponding to the first k largest eigenvalues ​​of the covariance matrix.

[0095] The embodiment of the present invention is based on the principal component analysis module, which converts high-dimensional raw data into low-dimensional principal component index vectors, retains key features, and discards redundant information, which helps to improve the efficiency and accuracy of subsequent function model fitting and cost prediction. In the subsequent model fitting process, the function model fitting module uses low-dimensional principal component index vectors to construct cost function models. These models can more accurately reflect the hidden patterns and trends in historical data. The current index acquisition module standardizes and projects the data of the current project into the principal component space to generate the principal component index vector of the current project. Finally, the unit price prediction module and the cost calculation module use the principal component index vector to calculate the cost of the new project through the existing cost function model.

[0096] Furthermore, if Figure 2As shown, the function model fitting module includes a historical data classification module, a historical principal component acquisition module and a genetic fitting module;

[0097] The historical data classification module is used to obtain historical project data and divide the historical project data into several historical project data groups according to the type of attributes. For example, the historical project data of a series of construction projects include the following key attributes: project number, project type (such as residential, commercial office, industry), geographical location (city or region), building area, material type (such as reinforced concrete, steel structure, wooden structure), construction start year, completion year, total construction cost, etc. For the above historical project data, the historical project data are divided into several historical project data groups according to the above key attributes to ensure that the projects in each historical project data group have similarities in these key attributes. Through the historical data classification module, a large amount of complex historical project data can be effectively classified into project data groups with their own similarities. This makes the subsequent principal component analysis more accurate and efficient, reduces the interference of noise, and improves the accuracy of model fitting.

[0098] The historical principal component acquisition module is used to obtain the historical principal component index vector corresponding to each historical project data set by calling the principal component analysis module. By calling the principal component analysis module, each historical project data set is subjected to principal component analysis, generating a principal component index vector for each historical project data set. This principal component index vector is then transferred to the genetic fitting module for further analysis and model fitting. Using the historical principal component acquisition module, the solution performs principal component analysis on each project data set to extract key features. This not only reduces data dimensionality and improves computational efficiency, but also enhances data interpretability, allowing key influencing factors to be identified and quantified.

[0099] The genetic fitting module is used to construct a principal component cost function model corresponding to each historical principal component index vector based on the historical principal component index vector and the genetic programming algorithm, and store the principal component cost function model and its corresponding model principal component index vector. In order to facilitate distinction, the historical principal component index vector used to construct the principal component cost function model is recorded as the model principal component index vector in the present invention. The genetic fitting module of this embodiment automatically generates a principal component cost function model that meets the requirements through the genetic programming algorithm, and can find the optimal or approximately optimal solution in a wide search space, reduce human intervention and bias, and improve the accuracy and reliability of the cost estimation model.

[0100] Genetic programming (GP) is an optimization technique based on genetic algorithms. Its goal is to automatically generate computer programs or function models that meet specific requirements. By simulating the processes of natural selection and biological evolution, GP searches for optimal or near-optimal solutions in a vast problem space.

[0101] Furthermore, if Figure 3 As shown, in the cost index data platform of the present invention, the construction of the principal component cost function model corresponding to each historical principal component index vector includes the following steps:

[0102] S1. Obtain the historical principal component index vector corresponding to each historical project data group;

[0103] S2. Randomly generate an initial population; each population represents a principal component cost function model; the principal component cost function model adopts a tree structure, and the nodes in the tree structure represent mathematical operators or attribute values ​​corresponding to principal component indicators; the principal component indicators are obtained through historical principal component indicator vectors, and the attribute values ​​corresponding to the principal component indicators are obtained through historical project data groups; mathematical operators may include addition, multiplication, exponentiation, etc.

[0104] S3. Evaluate the fitness function value of each population for the historical data set based on the fitness function; the fitness function is used to evaluate the error between the actual cost value and the predicted cost value, that is, the fitness function value. Specifically, the fitness function is expressed as:

[0105]

[0106] Among them, E represents the preset fitness function value range, A i is the actual cost value of the population for the i-th sample of the historical project data set, P i is the cost prediction value of the population for the i-th sample of the historical project data set; n is the number of samples in the historical project data set.

[0107] In this embodiment, limiting the fitness value can prevent the problem of numerical instability caused by abnormally large or small fitness values, and in the fitness-based selection process (such as roulette selection), limiting the range ensures that each individual has a relatively balanced chance of entering the next generation, avoiding abnormally high fitness individuals dominating the selection process.

[0108] S4. Determine a candidate population for genetic operations based on the fitness function and a selection algorithm; perform replication, mutation, transposition, and / or recombination operations on the candidate population; the selection algorithm employs roulette wheel selection or tournament selection. Replication involves directly copying individuals from the candidate population to the next generation, without modifying them in any way, but retaining the current individuals. This replication ensures that high-quality individuals are retained, allowing their superior traits to be passed on to the next generation. In practice, replication is performed based on fitness selection, with individuals with high fitness being more likely to be replicated. Mutation involves randomly modifying the genetic sequence of a single individual, introducing new genotypes, increasing population diversity, and preventing premature convergence to a local optimum. Recombination, also known as recombination, involves exchanging gene segments between two or more individuals to create new individuals. Recombination includes single-point recombination, two-point recombination, and gene recombination. Single-point recombination involves selecting a recombination point and exchanging the genes following that point; two-point recombination involves selecting two recombination points and exchanging the genetic sequence between them; and gene recombination involves selecting from both parents at each gene position with a certain probability.

[0109] S5. Repeat steps S3 and S4 until the termination condition is met, and store the optimal principal component cost function model and its corresponding historical principal component index vector. In this way, each principal component cost function model can accurately simulate the relationship between the historical project data set and the cost, and can be used for project cost forecasting.

[0110] This embodiment utilizes the evolutionary mechanism of the genetic algorithm to perform replication, mutation, transposition, and recombination operations based on fitness evaluation, ensuring that the final model performs well in terms of error minimization and generates a principal component cost function model that conforms to the actual project.

[0111] Furthermore, the unit price prediction module includes a prediction index determination module and a price prediction module;

[0112] The prediction indicator determination module is used to determine the market pricing indicators within the current principal component indicator vector. In this embodiment, the principal component indicators can be set to either a quota pricing or market pricing model. Quota pricing is a method for calculating construction costs based on pre-established quota standards, unit labor hours, unit material consumption, and other factors, combined with baseline price levels over a specific period. Market pricing is a method for calculating project costs in real time based on current market prices for materials, labor costs, machinery rental fees, and other factors. The prediction indicator determination module selects indicators from the current principal component indicator vector that utilize a market pricing model and are sensitive to market price fluctuations.

[0113] The price prediction module is used to obtain predicted unit prices for each market pricing indicator based on a price prediction model. The price prediction model can be based on machine learning or a time series analysis model. The model inputs the historical unit price time series corresponding to the market pricing indicator into the price prediction model, outputs the corresponding predicted unit price curve, and uses this to obtain the predicted unit price corresponding to the market pricing time point set in the predicted unit price curve. By combining the predicted unit price for each market pricing indicator with the standard unit price corresponding to the quota pricing indicator, the complete predicted unit price for each principal component indicator can be obtained.

[0114] Furthermore, the cost calculation module includes a principal component matching module, an attribute value compensation module, a first calculation module and a second calculation module;

[0115] The principal component matching module is used to obtain the model principal component index vector with the highest matching degree based on the current principal component index vector; the principal component matching module is responsible for finding the principal component index vector of the historical model that best matches the current principal component index vector, which is specifically achieved by calculating cosine similarity and other methods. The goal is to find the historical model that is closest to the characteristics of the current project so that further calculations can be performed using its corresponding cost function.

[0116] The attribute value compensation module is used to obtain the first attribute value corresponding to each principal component indicator in the current principal component indicator vector based on the current project data group; the first attribute value can be understood as the initial measurement of the current project on each principal component indicator, and these attribute values ​​are used for further calculation and compensation.

[0117] The first calculation module is used to compensate the first attribute value based on the predicted unit price corresponding to each principal component indicator to obtain a second attribute value. Specifically, the first calculation module is used to calculate the error ratio between the predicted unit price and the standard unit price of each principal component indicator. The error ratio represents the change in the predicted value relative to the standard value. The standard unit price is determined when the function model fitting module constructs the principal component cost function model and represents the uniform price prevailing for a certain material or service in the historical project data set within a set time period. The error ratio is then multiplied by the first attribute value to obtain the second attribute value corresponding to each principal component indicator. The second attribute value is the compensated value.

[0118] The second calculation module is used to input the second attribute value corresponding to each principal component index into the principal component cost function model corresponding to the model principal component index vector to obtain the cost calculation result. The second attribute value corresponding to each principal component index is input into the principal component cost function model of the matching model principal component index vector to calculate the final cost calculation result. The cost calculation result obtained based on the second attribute value is a more accurate cost estimate after being adjusted by market dynamics.

[0119] In this embodiment, the principal component cost function model predicts the cost calculation result based on the attribute value corresponding to the principal component index. Since the input of the principal component cost function model is the attribute value, it is impossible to compensate the error of the model output by inputting the predicted unit price and the standard unit price. Therefore, the embodiment of the present invention obtains the first attribute value through the current project data group, and then calculates the error ratio of the predicted unit price and the standard unit price and the product of the first attribute value as the second attribute value, so as to dynamically compensate the output of the model without modifying the original model.

[0120] This embodiment introduces dynamic changes in the market pricing model through the attribute value compensation module and the first calculation module, and inputs the compensated second attribute value into the principal component cost function model to dynamically compensate the output of the model without modifying the original model, so that the cost calculation results output by the principal component cost function model can dynamically respond to changes in market prices and be more in line with the predicted market conditions, thereby improving the robustness and applicability of the model in different market environments.

[0121] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, each functional module may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0122] Example 2

[0123] An electronic device 2, such as Figure 4 As shown, a processor 21 and a memory 22 are provided. The memory 22 is used to store computer program codes. The computer program codes include computer instructions. When the processor 21 executes the computer instructions, the electronic device executes the above-mentioned cost index data platform.

[0124] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, memory 22, input device 24, and output device 23 are coupled via a connector, which may include various interfaces, transmission lines, or buses, etc., although this is not limited in the present embodiment. It should be understood that in various embodiments of the present invention, coupling refers to interconnection in a specific manner, including direct connection or indirect connection through other devices, such as various interfaces, transmission lines, buses, etc.

[0125] The processor 21 may be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Alternatively, the processor 21 may be a processor group consisting of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Alternatively, the processor may be another type of processor, and the embodiments of the present invention are not limited thereto.

[0126] The memory 22 can be used to store computer program instructions and various computer program codes, including program codes for executing the embodiments of the present invention. Optionally, the memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for related instructions and data.

[0127] The input device 24 is used to input data and / or signals, and the output device 23 is used to output data and / or signals. The output device 23 and the input device 24 can be independent devices or an integrated device.

[0128] This application achieves preliminary risk assessment by fitting a generalized autoregressive conditional heteroskedasticity model corresponding to each digital asset, obtaining the conditional variance of each digital asset at several set time points based on the generalized autoregressive conditional heteroskedasticity model, and then calculating the value at risk. A first dependency matrix between the values ​​at risk of each digital asset is constructed, and an error compensation model is constructed and trained based on risk influencing factors and an error analysis matrix. The first dependency matrix is ​​corrected according to the error compensation model to obtain a second dependency matrix, thereby achieving error compensation for the dependencies and thus achieving accurate dependency prediction. By combining the second dependency matrix with the generalized autoregressive conditional heteroskedasticity model of each digital asset, accurate risk prediction of the digital assets can be achieved, thereby improving the overall risk prediction capability.

[0129] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A cost index data platform, characterized by: It includes a principal component analysis module, a function model fitting module, a current index acquisition module, a unit price prediction module and a cost calculation module with communication connection; The principal component analysis module is used to obtain a standardized indicator matrix based on the project data group, and obtain a principal component indicator vector based on the standardized indicator matrix; The function model fitting module is configured to obtain a plurality of historical project data groups, obtain a historical principal component index vector corresponding to each historical project data group by calling a principal component analysis module, construct a principal component cost function model corresponding to each historical principal component index vector using a genetic algorithm, and store the principal component cost function model and its corresponding model principal component index vector; The current indicator acquisition module is configured to organize the current project data into a current project data group, and obtain the current principal component indicator vector corresponding to the current project data group by calling the principal component analysis module; The unit price prediction module is configured to obtain the predicted unit price corresponding to each principal component indicator in the current principal component indicator vector; the unit price prediction module includes a prediction indicator determination module and a price prediction module; the prediction indicator determination module is used to determine the market pricing indicator in the current principal component indicator vector; the price prediction module is used to obtain the predicted unit price of each market pricing indicator based on a price prediction model; the price prediction model is based on a machine learning model or a time series analysis model; The cost calculation module is configured to match the model principal component index vector according to the current principal component index vector, and obtain the cost calculation result according to the principal component cost function model corresponding to the model principal component index vector and the predicted unit price corresponding to each principal component index; The cost calculation module includes a principal component matching module, an attribute value compensation module, a first calculation module and a second calculation module; The principal component matching module is used to obtain the model principal component index vector with the highest matching degree according to the current principal component index vector; The attribute value compensation module is used to obtain the first attribute value corresponding to each principal component index in the current principal component index vector according to the current project data group; The first calculation module is used to compensate the first attribute value according to the predicted unit price corresponding to each principal component index to obtain the second attribute value; The second calculation module is used to input the second attribute value corresponding to each principal component index into the principal component cost function model corresponding to the model principal component index vector to obtain the cost calculation result.

2. A cost index data platform according to claim 1, characterized in that: The principal component analysis module includes a preprocessing module, a standardization conversion module, a normalization conversion module, a covariance calculation module and a principal component acquisition module; The preprocessing module is used to perform data cleaning and data screening on the project data group to obtain a first project data matrix; the standardization conversion module is used to perform standardization processing on the first project data matrix to obtain a second project data matrix; the normalization conversion module is used to perform normalization processing on the second project data matrix to obtain a third project data matrix; the covariance calculation module is used to obtain the covariance matrix of the third project data matrix; the principal component acquisition module is used to obtain the eigenvectors of the covariance matrix and their corresponding eigenvalues, and select several eigenvectors according to the eigenvalues ​​to construct a principal component index vector.

3. A cost index data platform according to claim 2, characterized in that: The principal component index vector is expressed as: ， ， in, represents the principal component index vector, represents the 1st to kth principal component index, represents the second project data matrix, represents the eigenvector of the covariance matrix, Represents the eigenvalue corresponding to the eigenvector, m represents the number of eigenvectors of the covariance matrix; k represents the number of eigenvectors of the characteristic matrix; the characteristic matrix is ​​constructed according to the eigenvectors corresponding to the first k largest eigenvalues ​​of the covariance matrix.

4. A cost index data platform according to claim 1, characterized in that: The function model fitting module includes a historical data classification module, a historical principal component acquisition module and a genetic fitting module; The historical data classification module is used to obtain historical project data and divide the historical project data into several historical project data groups according to the type of attributes; the historical principal component acquisition module is used to obtain the historical principal component index vector corresponding to each historical project data group by calling the principal component analysis module; the genetic fitting module is used to construct a principal component cost function model corresponding to each historical principal component index vector based on the historical principal component index vector and the genetic programming algorithm, and store the principal component cost function model and its corresponding model principal component index vector.

5. A cost index data platform according to claim 4, characterized in that: The method of constructing a principal component cost function model corresponding to each historical principal component index vector includes the following steps: S1. Obtain the historical principal component index vector corresponding to each historical project data group; S2. Randomly generate an initial population; each population represents a principal component cost function model; S3, evaluating the fitness function value of each population for the historical data set according to the fitness function; S4. Determine a candidate population for genetic operation based on the fitness function value and the selection algorithm; perform replication, mutation, transposition and / or recombination operations on the candidate population; S5. Repeat steps S3 and S4 until the termination condition is met, and store the best performing principal component cost function model and its corresponding historical principal component index vector.

6. A cost index data platform according to claim 5, characterized in that: The fitness function is expressed as: ， Among them, E represents the preset range of fitness function values, is the actual cost value of the population for the i-th sample of the historical project data set, is the cost prediction value of the population for the i-th sample of the historical project data set; n is the number of samples in the historical project data set.

7. A cost index data platform according to claim 5, characterized in that: The principal component cost function model adopts a tree structure, and the nodes in the tree structure represent mathematical operators or attribute values ​​corresponding to principal component indicators; the principal component indicators are obtained through historical principal component indicator vectors, and the attribute values ​​corresponding to the principal component indicators are obtained through historical project data groups.

8. The cost index data platform according to claim 1, characterized in that: The project data group stores a number of project numbers and their corresponding attributes and standard unit prices corresponding to the attributes through a tree structure; the project data group is obtained through a natural language processing model.

Images