Construction cost data query method and system for constructional engineering

Abstract

The invention relates to the technical field of data processing, in particular to a construction cost data query method and system for constructional engineering, and the method comprises the steps: obtaining the cost of each unit, constructing a full-connection graph according to the correlation degree of any two unit costs, carrying out the region division of the full-connection graph, obtaining a plurality of communities, and carrying out the query of construction cost data. The method comprises the following steps: performing principal component analysis on unit cost in each community to obtain core unit cost in each community, obtaining association degree based on association between the unit cost of the community, obtaining index construction confidence according to the association degree, and finally constructing a relation index according to the index construction confidence. The workload and the query difficulty of project cost query can be reduced, and the project cost query efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a construction engineering cost data query method and system. Background Art

[0002] Project cost data query is an important part of construction project management, and plays a key role in ensuring the budget, cost control and resource allocation of engineering projects. Existing project cost data mainly comes from various databases, industry standards and historical project data accumulation, but these data often have problems such as untimely updates and insufficient accuracy. Through big data, artificial intelligence and other technical means, automatic data update, accurate matching and intelligent recommendation can be achieved.

[0003] The construction cost data of construction projects is stored in a relational database in the form of cost reports. Different relational databases are connected through relational indexes. However, construction is a long process, and the market is dynamically changing. Therefore, as the progress of construction changes, the construction cost will also change in real time. Therefore, it is necessary to realize real-time estimation and measurement of the construction cost in this process. However, the relational database is pre-built based on the construction project. If the relational database needs to be reconstructed, the workload is extremely large, and the relational structure in the pre-built database (there are relationships between different engineering projects, so they need to be connected) cannot be added with market changes, resulting in great difficulty in querying related data or projects when estimating and calculating the construction cost. Summary of the invention

[0004] In order to solve the technical problem that the existing engineering cost data query is difficult, the purpose of the present invention is to provide a method and system for querying engineering cost data for construction projects. The technical solution adopted is as follows: In a first aspect of the present invention, a method for querying construction cost data for construction projects is provided, comprising: Obtaining each unit cost, where the unit cost is each cost of each construction project; According to the correlation degree between any two unit costs, a fully connected graph is constructed, and the fully connected graph is divided into regions to obtain multiple communities; Conduct principal component analysis on the unit costs within each community to obtain the core unit costs within each community; Based on the correlation between each unit cost of each community and each core unit cost of other communities, the correlation degree between each unit cost of each community and each core unit cost of other communities is obtained; The correlation between each unit cost of each community and each core unit cost of other communities is integrated to obtain the confidence of index construction between each unit cost of each community and other communities; A confidence level is constructed based on the index, and a relationship index of each community is constructed.

[0005] In an exemplary embodiment, the process of obtaining the correlation degree between any two unit costs includes: According to the time interval between the construction periods of any two unit costs, the consistency of the construction periods of any two unit costs is obtained; Based on the consistency of the construction period, combined with whether any two unit costs are in the same construction project and the similarity of any two unit costs, the correlation degree of any two unit costs is obtained.

[0006] In an exemplary embodiment, based on the correlation between each unit cost of each community and each core unit cost of other communities, the correlation degree between each unit cost of each community and each core unit cost of other communities is obtained, including: Obtaining, in the process of performing principal component analysis on each unit cost in the first target community, regression parameters of each unit cost in the first target community when constituting a linear combination of each core unit cost in the first target community; the first target community is any community; According to the regression parameters and the preset starting weights, weighted regression parameters of each unit cost in the first target community for each core unit cost are obtained; Obtaining similarities between each unit cost in the first target community and each unit cost in the second target community; the second target community is any community and is different from the first target community; According to the weighted regression parameters and the similarity, the correlation degree between each unit cost in the second target community and each core unit cost in the first target community is obtained.

[0007] In an exemplary embodiment, the correlation degree between each unit cost in the second target community and each core unit cost in the first target community is obtained according to the weighted regression parameter and the similarity, including: Obtaining, according to the weighted regression parameters and similarities of each unit cost in the first target community with respect to each core unit cost, the association sub-degrees corresponding to each unit cost in the first target community; The correlation sub-degrees corresponding to the unit costs in the first target community are integrated to obtain the correlation degree between the unit costs in the second target community and the core unit costs in the first target community.

[0008] In an exemplary embodiment, the calculation formula of the association degree is as follows: ; in, represents the degree of association between the i-th unit cost in the second target community and the j-th core unit cost in the K-th community, is the number of unit costs in the Kth community, which corresponds to the first target community; is the regression parameter of the mth unit cost in the Kth community when it constitutes the linear combination of the jth core unit cost in the Kth community, is the preset start weight of the mth unit cost in the Kth community. If the construction period of the mth unit cost has started at the current moment, then the preset start weight of the mth unit cost is recorded as 1, otherwise it is recorded as 0; is the cost sequence of the i-th unit cost in the second target community, is the cost sequence of the mth unit cost in the Kth community, express and The similarity of Represents the normalization function.

[0009] In an exemplary embodiment, the correlation between each unit cost of each community and each core unit cost of other communities is integrated to obtain the index construction confidence between each unit cost of each community and other communities, including: Obtaining the maximum value of the correlation degree between each unit cost in the second target community and each core unit cost in the first target community; Obtaining the remaining correlation degrees, except the maximum value, among the correlation degrees between each unit cost in the second target community and each core unit cost in the first target community; According to the difference between the maximum value and the remaining association degrees, the index construction confidence of each unit cost in the second target community and the first target community is determined.

[0010] In an exemplary embodiment, the calculation formula for the index construction confidence is as follows: ; in, represents the confidence of the i-th unit cost in the second target community and the index construction of the K-th community, represents the maximum value of the correlation between the i-th unit cost in the second target community and all the core unit costs in the K-th community, represents the number of core unit costs in the Kth community except the core unit cost corresponding to the maximum value, represents the core unit cost of the Kth community except the core unit cost corresponding to the maximum value. Core unit cost, represents the difference between the i-th unit cost in the second target community and the k-th unit cost in the K-th community. The degree of correlation between the costs of the core units.

[0011] In an exemplary embodiment, building confidence according to the index and building a relationship index for each community includes: Compare the individual unit costs of each community with the index-building confidence of each other community and the preset threshold; An index having a confidence level greater than the preset threshold is constructed to construct a relational index of a database corresponding to the community where the unit cost is located.

[0012] In an exemplary embodiment, the fully connected graph is divided into regions to obtain multiple communities, including: The Louvain algorithm is used to divide the fully connected graph into regions to obtain multiple communities.

[0013] In the second aspect of the present invention, a construction engineering cost data query system is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-mentioned construction engineering cost data query method when the program instructions are executed.

[0014] The present invention has the following beneficial effects: the present invention first constructs a fully connected graph according to the correlation between any two unit costs, and divides the fully connected graph into regions to obtain multiple communities, so as to accurately construct a relational index for each community with respect to a relational database. Then, in order to reduce the workload of data processing and analyze the relevant unit costs in a targeted manner, the unit costs in each community are subjected to principal component analysis to obtain the core unit costs in each community. Then, according to the correlation between each unit cost of each community and each core unit cost of other communities, the correlation between each unit cost of each community and each core unit cost of other communities is obtained. Thus, based on the correlation, the confidence of each unit cost of each community and the index construction of other communities is obtained. Finally, the confidence is constructed according to the index, and the relational index of each community is constructed. In this way, the workload and query difficulty of engineering cost query can be reduced, and the efficiency of engineering cost query can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of factors affecting the project cost in the project settlement process provided by an embodiment of the present invention; Figure 2It is a flow chart of a construction engineering cost data query method provided by an embodiment of the present invention; Figure 3 is a flowchart of obtaining the degree of relevance provided by an embodiment of the present invention; Figure 4 is a flowchart of obtaining the degree of association provided by an embodiment of the present invention; Figure 5 is a specific implementation flow chart of step 4-4 provided by an embodiment of the present invention; Figure 6 is a flowchart of obtaining index construction confidence provided by an embodiment of the present invention; Figure 7 This is a flowchart of a relational index construction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] Normally, engineering cost involves the following stages: investment estimation (project proposal and feasibility study stage); design budget (preliminary design stage), in which it is necessary to build an initial relational database for the quotations and relationships between various engineering projects based on experience and market prices; revised budget (technical design stage, technical design can be carried out as needed for major projects or technically complex projects); construction drawing budget (construction drawing design stage); maximum bid limit / tender control price (tender stage); contract price (contract signing stage); engineering settlement (contract implementation stage), engineering settlement mainly involves the completion of each construction stage or construction project. This step is mainly to estimate the expenses required for subsequent projects based on current expenses, market prices, etc., which is mainly manifested in engineering changes of design plans, specifically in budget reduction, adjustment of non-essential expenses, etc. Although there are many types of engineering changes and the reasons for their occurrence are also various, they are essentially changes in price and quantity; completion settlement (completion acceptance stage).

[0019] Factors that affect the project cost during the project settlement process (the impact of project changes), such as Figure 1As shown, most of the cases are due to design changes.

[0020] This embodiment provides a method for querying construction cost data for construction projects. The main purpose of the method is to add indexes between databases with relationships in the relational database on the basis of the original relational database, so as to improve the convenience of querying construction cost data and reduce the difficulty of querying.

[0021] like Figure 2 As shown, the engineering cost data query method includes the following steps: Step 1: Obtain each unit cost, which is each cost of each construction project; Step 2: According to the correlation between any two unit costs, a fully connected graph is constructed and the fully connected graph is divided into regions to obtain multiple communities; Step 3: Conduct principal component analysis on the unit costs in each community to obtain the core unit costs in each community; Step 4: Based on the correlation between each unit cost of each community and each core unit cost of other communities, the correlation degree between each unit cost of each community and each core unit cost of other communities is obtained; Step 5: Integrate the correlation between each unit cost of each community and each core unit cost of other communities to obtain the confidence of each unit cost of each community and the index construction of other communities; Step 6: Build confidence based on the index and construct the relationship index of each community.

[0022] The implementation process of each step is described in detail as follows.

[0023] Step 1: Obtain each unit cost, which is each cost item in each construction project.

[0024] The construction project plan includes multiple construction projects and the estimated cost of each construction project. A complete construction project includes several costs, each of which is a unit cost. Therefore, the construction project plan includes multiple construction projects, and each construction project includes multiple unit costs. Therefore, the unit costs are obtained according to the construction project plan.

[0025] The construction project plan is the daily consumption of various unit costs. If the unit costs of different stages and projects are consistent, the unit costs shall be recorded in the order in which they appear in the construction project plan to avoid the situation where multiple unit costs are exactly the same.

[0026] It should be understood that the unit cost involves multiple cost data (i.e. cost values) of corresponding items in the construction project. For example, the corresponding construction period is divided into multiple time periods (such as every day), and a cost data is obtained in each time period, thereby obtaining multiple cost data involved in the unit cost. Therefore, the unit cost is essentially a cost sequence composed of multiple cost data.

[0027] The construction cost data query method is used to solve the following existing problems: since different unit costs in a complete construction project may have different starting times and inconsistent time sequences, it leads to a surge in the difficulty of building a relational database, and the mismatch in time during query increases the difficulty of building an index.

[0028] Step 2: According to the correlation between any two unit costs, a fully connected graph is constructed and the fully connected graph is divided into regions to obtain multiple communities.

[0029] The construction project plan can only reflect the expected cost consumption of the project. Therefore, it is necessary to build an initial relational database based on the relationship between them, that is, the existing covariant relationship. This embodiment constructs a fully connected graph according to the correlation between any two unit costs, thereby analyzing different unit costs to build an initial relational database.

[0030] In an exemplary embodiment, Figure 3 As shown, the process of obtaining the relevant degree includes: Step 2-1: According to the time interval between the construction periods of any two unit costs, obtain the consistency of the construction periods of any two unit costs.

[0031] The first unit cost and the second unit cost are set as any two unit costs. It should be understood that the longer the time interval between the construction periods of the first unit cost and the second unit cost, that is, the farther the construction periods of the two unit costs differ in time, the less consistent the construction periods between the two unit costs are, that is, the lower the consistency of the construction periods. Therefore, according to the time interval between the construction periods of the first unit cost and the second unit cost, the consistency of the construction periods of the first unit cost and the second unit cost is obtained, and the consistency of the construction periods is inversely proportional to the time interval. In an exemplary embodiment, the calculation formula for the consistency of the construction periods of the first unit cost and the second unit cost is as follows: ; in, Indicates the consistency of the duration between the first unit cost and the second unit cost, It represents the time interval between the duration of the first unit cost and the duration of the second unit cost. Represents an exponential function with the natural constant e as the base, used for Negative correlation normalization.

[0032] The time interval means: assuming that the duration of the first unit cost is earlier than the duration of the second unit cost, then the time interval between the durations of the first unit cost and the second unit cost is the time interval between the end time of the duration of the first unit cost and the start time of the duration of the second unit cost. If the end time of the duration of the first unit cost is later than the start time of the duration of the second unit cost, the time interval between the durations of the first unit cost and the second unit cost is set to 0.

[0033] Step 2-2: Based on the consistency of the construction period, combined with whether any two unit costs are in the same construction project and the similarity of any two unit costs, the correlation degree of any two unit costs is obtained.

[0034] The judgment coefficient of the first unit cost and the second unit cost is set to represent whether the first unit cost and the second unit cost are in the same construction project. If the first unit cost and the second unit cost are in the same construction project, the judgment coefficient of the first unit cost and the second unit cost is 1; if the first unit cost and the second unit cost are not in the same construction project, the judgment coefficient of the first unit cost and the second unit cost is 0.

[0035] The similarity between the first unit cost and the second unit cost is obtained. In an exemplary embodiment, the similarity between the first unit cost and the second unit cost is specifically the Pearson correlation coefficient between the first unit cost and the second unit cost. As other implementation methods, other similarity calculation methods such as cosine similarity between the first unit cost and the second unit cost can also be used. It should be understood that due to objective factors, such as different construction period lengths, the number of cost data included in the first unit cost and the second unit cost is different. Therefore, other shorter unit costs (i.e., unit costs containing fewer cost data) can also be interpolated to make the lengths of the first unit cost and the second unit cost the same. In addition, if the interpolation operation is not performed, the DTW distance between the first unit cost and the second unit cost can also be obtained, and then the obtained DTW distance is negatively correlated to obtain similarity.

[0036] It should be understood that no matter what similarity calculation method is used, after obtaining the similarity, it needs to be normalized for subsequent processing. The normalization in this embodiment and the norm normalization function can be specifically set according to actual conditions, for example, the maximum and minimum normalization method can be used, or the following common method can be used: , It represents the processing object, and exp represents the exponential function with the natural constant e as the base.

[0037] Therefore, according to the consistency of the construction period of the first unit cost and the second unit cost, combined with whether the first unit cost and the second unit cost are in the same construction project, and the similarity of the first unit cost and the second unit cost, the correlation degree between the first unit cost and the second unit cost is obtained, and the calculation formula is as follows: in, is the correlation between the first unit cost and the second unit cost, is the judgment coefficient of the first unit cost and the second unit cost, is the normalized similarity between the first unit cost and the second unit cost.

[0038] The degree of correlation can indicate the mutual influence relationship between the first unit cost and the second unit cost. The larger the value, the greater the influence relationship. The closer the value is to 0, the less correlation there is between the first unit cost and the second unit cost.

[0039] Therefore, according to the correlation degree between any two unit costs, a fully connected graph is constructed, where each node in the fully connected graph is each unit cost, and the degree between any two nodes is the correlation degree.

[0040] Then, the fully connected graph is divided into regions to obtain multiple communities. In an exemplary embodiment, the Louvain algorithm is used to divide the fully connected graph into regions to obtain multiple communities. There are several unit costs in each community, and there are relationships between them.

[0041] Step 3: Conduct principal component analysis on the unit costs within each community to obtain the core unit costs within each community.

[0042] The same community contains several unit costs, which are similar or in the same construction project, and some unit costs are affected by other unit costs and change. Therefore, if all unit costs in the community are analyzed, it is not realistic for the huge construction project. Therefore, this embodiment analyzes the unit costs in the same community and only obtains the source unit costs as the core unit costs for analysis.

[0043] Set the first target community to be any community. Interpolate the construction period of all unit costs in the first target community. Interpolate all unit costs in the first target community according to the longest construction period of the unit cost in the first target community, where the interpolated object is the cost data of each day within the construction period of the unit cost. Make the construction period of each unit cost in the first target community the same.

[0044] Then, the PCA algorithm is used to perform principal component analysis on all unit costs in the first target community for dimensionality reduction, and several principal component directions are obtained. These principal component directions are linear combinations of the daily cost data of multiple unit costs during the construction period. Each principal component direction represents a source of unit cost influencing factors. When the unit cost in the principal component direction changes, other related unit costs will also change accordingly.

[0045] The PCA algorithm is used to perform principal component analysis on all unit costs in the first target community to obtain the unit costs corresponding to each principal component direction in the first target community, and these unit costs are defined as core unit costs.

[0046] Step 4: Based on the correlation between each unit cost of each community and each core unit cost of other communities, the correlation degree between each unit cost of each community and each core unit cost of other communities is obtained.

[0047] Due to the difference between the specific construction links and the construction project plan, and the need to coordinate the construction of a unit cost with other unit costs in the project during its construction period, there will be situations where the construction period is completed ahead of schedule or delayed, resulting in the difference between the daily cost consumption and the plan in the actual construction process. Although this change will affect the recording of the project cost data, its change can better highlight the relationship with other unit costs that are not in the same community. Therefore, this embodiment analyzes the covariation relationship between the corresponding unit cost and other related unit costs in the construction period up to the current date, and then analyzes the hidden cost data change relationship to obtain the index construction confidence. Before obtaining the index construction confidence, it is necessary to obtain the degree of correlation between each unit cost of each community and each core unit cost of other communities based on the correlation between each unit cost of each community and each core unit cost of other communities.

[0048] In an exemplary embodiment, Figure 4 As shown in FIG. 1 , the process of obtaining the degree of association includes: Step 4-1: Obtain regression parameters of each unit cost in the first target community when constituting a linear combination of each core unit cost in the first target community in the process of performing principal component analysis on each unit cost in the first target community.

[0049] Based on the core principle of the PCA algorithm, the core unit cost in the first target community is a linear combination of all unit costs in the first target community. Therefore, in the process of principal component analysis of each unit cost in the first target community, the regression parameters of each unit cost in the first target community when constituting the linear combination of each core unit cost in the first target community are obtained. Therefore, the contribution of each unit cost in the first target community to each core unit cost in the first target community depends on the size of its regression parameter.

[0050] Step 4-2: According to the regression parameters and the preset starting weights, the weighted regression parameters of each unit cost in the first target community for each core unit cost are obtained.

[0051] A starting weight is preset, and each unit cost in the first target community corresponds to a preset starting weight. Any unit cost in the first target community is set as the mth unit cost. If the construction period of the mth unit cost has already started at the current moment, that is, during the construction period of the current date, the preset starting weight of the mth unit cost is recorded as 1, otherwise it is recorded as 0. Thus, the preset starting weights of each unit cost in the first target community are obtained.

[0052] According to the regression parameters and the preset starting weights, weighted regression parameters of each unit cost in the first target community for each core unit cost are obtained. In this embodiment, the regression parameters and the preset starting weights are multiplied, and the product obtained is used as the weighted regression parameter.

[0053] Step 4-3: Obtain the similarity between each unit cost in the first target community and each unit cost in the second target community.

[0054] The second target community is set to be any community that is different from the first target community. The similarity between each unit cost in the first target community and each unit cost in the second target community is obtained. The similarity may be a Pearson correlation coefficient, a cosine similarity, or the like.

[0055] Step 4-4: According to the weighted regression parameters and similarity, the correlation degree between each unit cost in the second target community and each core unit cost in the first target community is obtained.

[0056] In an exemplary embodiment, Figure 5 As shown, the specific implementation process of step 4-4 is as follows: Step 4-4-1: According to the weighted regression parameters and similarities of each unit cost in the first target community with respect to each core unit cost, the correlation sub-degree corresponding to each unit cost in the first target community is obtained.

[0057] Step 4-4-2: The correlation sub-degrees corresponding to each unit cost in the first target community are integrated to obtain the correlation degree between each unit cost in the second target community and each core unit cost in the first target community.

[0058] In an exemplary embodiment, the calculation formula of the association degree is as follows: ; in, represents the degree of association between the i-th unit cost in the second target community and the j-th core unit cost in the K-th community, represents the j-th core unit cost in the K-th community, is the number of unit costs in the Kth community, which corresponds to the first target community; is the regression parameter of the mth unit cost in the Kth community when it constitutes the linear combination of the jth core unit cost in the Kth community, is the preset start weight of the mth unit cost in the Kth community. If the construction period of the mth unit cost has started at the current moment, then the preset start weight of the mth unit cost is recorded as 1, otherwise it is recorded as 0; is the cost sequence of the i-th unit cost in the second target community, is the cost sequence of the mth unit cost in the Kth community, express and The similarity of Represents the normalization function.

[0059] It represents the reference weight of the regression parameter of the mth unit cost in the Kth community when constructing the linear combination of the jth core unit cost in the Kth community. The larger the value, the greater the reference significance of the mth unit cost in constructing the jth core unit cost. The larger the value of , the more important the m-th unit cost is and the greater the correlation of the change.

[0060] It represents the degree of association corresponding to the mth unit cost in the Kth community.

[0061] By adopting the above method, the correlation degree between each unit cost in the second target community and the j-th core unit cost in the K-th community is obtained, thereby obtaining the correlation degree between each unit cost in other communities and the j-th core unit cost in the K-th community, thereby obtaining the correlation degree between each unit cost in other communities and each core unit cost in each community.

[0062] Step 5: Integrate the correlation between each unit cost of each community and each core unit cost of other communities to obtain the confidence level of each unit cost of each community and the index construction of other communities.

[0063] If the Kth community contains multiple core unit costs, and each core unit cost must be linearly independent of each other, then when the correlation between the i-th unit cost in the second target community and the multiple core unit costs in the Kth community is not much different, it means that the correlation between the i-th unit cost and the K-th community is not strong enough, so there is no need to build an index. Then when the correlation between the i-th unit cost and one and only one core unit cost in the K-th community is greater, it is only related to one core unit cost, that is, the i-th unit cost is highly similar to only one core unit cost in the K-th community, and is very dissimilar to other core unit costs, and an index needs to be built. Therefore, this embodiment integrates the correlation between the i-th unit cost and the core unit costs of the K-th community to obtain the index construction confidence of the i-th unit cost and the K-th community.

[0064] In an exemplary embodiment, Figure 6 As shown, a specific process of obtaining the index construction confidence is given as follows: Step 5-1: Obtain the maximum value of the correlation degree between each unit cost in the second target community and each core unit cost in the first target community.

[0065] Step 5-2: Obtain the remaining correlation degrees except the maximum value among the correlation degrees between each unit cost in the second target community and each core unit cost in the first target community.

[0066] Step 5-3: Determine the confidence level of each unit cost in the second target community and the index construction of the first target community based on the difference between the maximum value and the remaining association degrees.

[0067] The greater the difference between the maximum value and the rest of the correlation degrees, it means that the unit cost in the second target community is only related to one core unit cost in the first target community, and the difference between this correlation degree and the correlation degrees of other core unit costs in the first target community is large, indicating that the unit cost in the second target community is strongly correlated with the first target community, so an index needs to be constructed.

[0068] In an exemplary embodiment, the calculation formula for index building confidence is as follows: ; in, represents the confidence of the i-th unit cost in the second target community and the index construction of the K-th community, represents the maximum value of the correlation between the i-th unit cost in the second target community and all the core unit costs in the K-th community, represents the number of core unit costs in the Kth community except the core unit cost corresponding to the maximum value, represents the core unit cost of the Kth community except the maximum value. Core unit cost, represents the difference between the i-th unit cost in the second target community and the k-th unit cost in the K-th community. The degree of correlation between the costs of the core units.

[0069] Indicates that, in addition to the largest degree of association, the i-th unit cost in the second target community is equal to the i-th unit cost in the K-th community. The mean correlation between the core unit costs.

[0070] It represents the maximum value of the correlation between the i-th unit cost in the second target community and all the core unit costs in the K-th community, and the difference between the correlation between the i-th unit cost in the second target community and all the core unit costs in the K-th community except the maximum value. The larger the value, the greater the difference between the largest core unit cost and other core unit costs, indicating that there is an index that needs to build a relationship.

[0071] Step 6: Build confidence based on the index and construct the relationship index of each community.

[0072] The greater the index building confidence, the more it is necessary to build a relationship index. In an exemplary embodiment, Figure 7 As shown in the figure, a specific construction process of the relational index is as follows: Step 6-1: Compare each unit cost of each community with the index construction confidence of other communities and a preset threshold.

[0073] Step 6-2: Construct an index greater than a preset threshold to construct a relational index of the database corresponding to the community where the confidence level corresponds to the unit cost.

[0074] A threshold is preset, and the range of the preset threshold is 0-1. The specific value of the preset threshold is set according to actual judgment needs, such as 0.68.

[0075] Compare the unit cost of each community with the index building confidence of other communities and the preset threshold. If it is greater than the preset threshold, it means that the index building confidence is large and a relationship index needs to be built; if it is less than or equal to the preset threshold, it means that the index building confidence is small and a relationship index does not need to be built.

[0076] The unit costs of each community and the index building confidences of other communities that are greater than a preset threshold value, as well as the unit costs of each community corresponding to these index building confidences, are obtained.

[0077] Construct confidence using the i-th unit cost in the second target community and the index of the K-th community For example, if the index build confidence If the value of the k-th community is greater than a preset threshold, then a relational index is constructed for the database corresponding to the second target community where the i-th unit cost is located in the database corresponding to the k-th community.

[0078] This completes the construction of the relational index.

[0079] This embodiment also provides a construction project-oriented engineering cost data query system, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-mentioned construction project-oriented engineering cost data query method embodiment when the program instructions are executed.

[0080] In an exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned embodiment of the construction cost data query method for construction projects.

[0081] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A construction cost data query method for construction projects, characterized in that: include: Obtaining each unit cost, where the unit cost is each cost of each construction project; According to the correlation degree between any two unit costs, a fully connected graph is constructed, and the fully connected graph is divided into regions to obtain multiple communities; Conduct principal component analysis on the unit costs within each community to obtain the core unit costs within each community; Obtaining, in the process of principal component analysis of each unit cost in the first target community, regression parameters of each unit cost in the first target community when constituting a linear combination of each core unit cost in the first target community; the first target community is any community; obtaining, according to the regression parameters and the preset starting weights, weighted regression parameters of each unit cost in the first target community for each core unit cost; obtaining similarities between each unit cost in the first target community and each unit cost in the second target community; The second target community is any community and is different from the first target community; according to the weighted regression parameter and the similarity, the correlation degree between each unit cost in the second target community and each core unit cost in the first target community is obtained; The correlation between each unit cost of each community and each core unit cost of other communities is integrated to obtain the confidence of index construction between each unit cost of each community and other communities; A confidence level is constructed based on the index, and a relationship index of each community is constructed.

2. A construction engineering cost data query method for construction engineering as claimed in claim 1, characterized in that: The process of obtaining the correlation degree between any two unit costs includes: According to the time interval between the construction periods of any two unit costs, the consistency of the construction periods of any two unit costs is obtained; Based on the consistency of the construction period, combined with whether any two unit costs are in the same construction project and the similarity of any two unit costs, the correlation degree of any two unit costs is obtained.

3. A construction engineering cost data query method for construction engineering as claimed in claim 1, characterized in that: According to the weighted regression parameters and similarity, the correlation between each unit cost in the second target community and each core unit cost in the first target community is obtained, including: Obtaining, according to the weighted regression parameters and similarities of each unit cost in the first target community with respect to each core unit cost, the association sub-degrees corresponding to each unit cost in the first target community; The correlation sub-degrees corresponding to the unit costs in the first target community are integrated to obtain the correlation degree between the unit costs in the second target community and the core unit costs in the first target community.

4. A construction cost data query method for construction projects as claimed in claim 3, characterized in that: The calculation formula of the degree of association is as follows: ； in, represents the degree of association between the i-th unit cost in the second target community and the j-th core unit cost in the K-th community, is the number of unit costs in the Kth community, which corresponds to the first target community; is the regression parameter of the mth unit cost in the Kth community when it constitutes the linear combination of the jth core unit cost in the Kth community, is the preset start weight of the mth unit cost in the Kth community. If the construction period of the mth unit cost has started at the current moment, then the preset start weight of the mth unit cost is recorded as 1, otherwise it is recorded as 0; is the cost sequence of the i-th unit cost in the second target community, is the cost sequence of the mth unit cost in the Kth community, express and The similarity of Represents the normalization function.

5. A construction engineering cost data query method for construction engineering as claimed in claim 1, characterized in that: The correlation between each unit cost of each community and each core unit cost of other communities is used to obtain the confidence of index construction between each unit cost of each community and other communities, including: Obtaining the maximum value of the correlation degree between each unit cost in the second target community and each core unit cost in the first target community; Obtaining the remaining correlation degrees, except the maximum value, among the correlation degrees between each unit cost in the second target community and each core unit cost in the first target community; According to the difference between the maximum value and the remaining association degrees, the index construction confidence of each unit cost in the second target community and the first target community is determined.

6. A construction engineering cost data query method for construction engineering as claimed in claim 5, characterized in that: The calculation formula for the index construction confidence is as follows: ； in, represents the confidence of the i-th unit cost in the second target community and the index construction of the K-th community, represents the maximum value of the correlation between the i-th unit cost in the second target community and all the core unit costs in the K-th community, represents the number of core unit costs in the Kth community except the core unit cost corresponding to the maximum value, represents the core unit cost of the Kth community except the core unit cost corresponding to the maximum value. The core unit cost, represents the difference between the i-th unit cost in the second target community and the k-th unit cost in the K-th community. The degree of correlation between the costs of the core units.

7. A construction engineering cost data query method for construction engineering as claimed in claim 1, characterized in that: The confidence is constructed according to the index, and the relationship index of each community is constructed, including: Compare the individual unit costs of each community with the index-building confidence of each other community and the preset threshold; An index having a confidence level greater than the preset threshold is constructed to construct a relational index of a database corresponding to the community where the unit cost is located.

8. A construction engineering cost data query method for construction engineering as claimed in claim 1, characterized in that: The fully connected graph is divided into regions to obtain multiple communities, including: The Louvain algorithm is used to divide the fully connected graph into regions to obtain multiple communities.

9. A construction cost data query system for construction projects, characterized by comprising: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the construction cost data query method for construction projects described in any one of claims 1 to 8 when the program instructions are executed.

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