Multi-dimensional modeling and comprehensive evaluation method for building turnover material turnover efficiency

Abstract

The application discloses a kind of building turnover material turnover efficiency multidimensional modeling and comprehensive evaluation method, it is characterized in that, the method mainly includes the following steps: step 1, the lease data modeling of building turnover material, establishes inventory data variation model, real-time inventory data model, project in and out account data model and project balance data model;Step 2: the lease data of building turnover material is reduced to data, and according to the turnover logic of turnover material constructs time-space sequence turnover efficiency multidimensional data model;Step 3: according to the dimension of multidimensional data model, the selection of index level is carried out, and the corresponding evaluation index is determined;Step 4: combined with the multidimensional index of building turnover material, the turnover efficiency comprehensive evaluation result is obtained by linear weighting method, to provide decision-making basis for enterprise managers.

Description

Technical Field

[0001] This invention relates to the technical field of efficient turnover of building turnover materials, specifically to a multi-dimensional modeling and comprehensive evaluation method for the turnover efficiency of building turnover materials. Background Technology

[0002] With the development of my country's construction industry, the application of construction turnover materials is widespread, and the rental market for turnover materials is growing rapidly. However, the variety and quantity of materials rented for construction projects are currently large, making material rental management increasingly complex for enterprises. In order to improve the turnover efficiency of construction materials and increase the market competitiveness of resource management companies, it is necessary to develop a reasonable rental management model and comprehensive evaluation method.

[0003] Current methods for handling building material rental still have many shortcomings. The most prominent issue is that many current data collection and analysis models and algorithms focus primarily on a single dimension, failing to provide multi-level comprehensive analysis and evaluation of turnover efficiency. Consequently, the resulting evaluations have little impact on improving turnover efficiency. Therefore, how to comprehensively analyze and evaluate building turnover efficiency remains a challenging problem. Summary of the Invention

[0004] To address the problems existing in the prior art, the purpose of this invention is to propose a multi-dimensional modeling and comprehensive evaluation method for the turnover efficiency of building turnover materials. This method determines the corresponding evaluation indicators through multi-dimensional fusion modeling, realizing multi-level comprehensive analysis and evaluation of turnover efficiency, and providing decision-making basis for enterprise managers.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] A multi-dimensional modeling and comprehensive evaluation method for the turnover efficiency of building materials includes the following steps:

[0007] Step 1, Data Modeling for Building Materials Rental:

[0008] To achieve data collection and storage, rental data modeling was first conducted. Since the rental process of building materials mainly includes four aspects of data: inventory data changes, real-time inventory data, project inflow and outflow data, and project balance data, models for inventory data changes, real-time inventory data, project inflow and outflow data, and project balance data were established for the above four aspects of data, respectively.

[0009] Step 2, Data Reduction and Multi-Dimensional Fusion Modeling:

[0010] First, the rental data of building turnover materials is simplified. Duplicate data is identified by using material numbers and the time of data changes. Erroneous data is identified by judging the data content constraints specified in a single dataset.

[0011] Secondly, in order to track information across multiple dimensions throughout the entire material leasing process and ensure that the status information of the materials can be accurately and quickly reflected at any stage of the leasing, it is necessary to integrate various datasets based on the flow logic of turnover materials and construct a multi-dimensional data model of spatiotemporal sequential turnover efficiency.

[0012] In this model, time is used as the node to integrate the three dimensions of material information, project information, and cost information, so that a single dataset can cover four dimensions: materials, project, cost, and time.

[0013]

[0014] In subsequent data analysis, specific evaluation systems and indicators can be formulated based on the multidimensional dataset provided by the model, parameters of one or two dimensions can be determined, the relationship between the changes of parameters in other dimensions can be analyzed, and corresponding evaluation results can be obtained.

[0015] Step 3, Key Indicator Extraction:

[0016] The selection of indicator levels is based on the dimensions of the multi-dimensional data model, that is, the corresponding evaluation indicators are determined through the material dimension, project dimension and cost dimension.

[0017] 1) Material-level evaluation indicators:

[0018] The real-time rental rate of a certain material is:

[0019] (6)

[0020] The average monthly rental rate for a certain material is:

[0021] (7)

[0022] The historical average rental rate for a certain material is:

[0023] (8)

[0024] 2) Project-level metrics:

[0025] The real-time percentage of a certain material in the project is as follows:

[0026] (9)

[0027] Percentage of a certain material rented in the project during the current month:

[0028] (10)

[0029] The historical rental percentage of a certain material in the project:

[0030] (11)

[0031] Historical rental percentage of all materials used in the project:

[0032] (12)

[0033] 3) Evaluation indicators based on cost:

[0034] The monthly accounting entries for different materials are as follows:

[0035] (13)

[0036] The monthly revenue details for different projects are as follows:

[0037] (14)

[0038] Step 4, Comprehensive evaluation of turnover efficiency based on multi-dimensional indicators:

[0039] Combining the aforementioned multi-dimensional indicators of building turnover materials, a comprehensive evaluation method for turnover efficiency can be formulated using a linear weighted method. Based on the indicators of each dimension, the following evaluation function is constructed:

[0040] (15)

[0041] in, This represents the calculated comprehensive index. Let represent the i-th analytical indicator function, and r represent the number of indicators considered by the decision-maker. ≥0 (i=1,2,3,…,m) are weight coefficients, and

[0042] (16)

[0043] A comprehensive turnover efficiency evaluation method can help companies analyze various evaluation results, such as the optimal inventory level for material preparation, the best cooperative target projects, and the maximum rental revenue for the company. Therefore, based on the company's required evaluation objectives, the weights of each objective value in the function can be reasonably allocated to obtain the comprehensive evaluation result of the company's required turnover efficiency, providing a basis for decision-making for company managers.

[0044] Compared with existing technologies, the present invention has the following advantages:

[0045] 1. The proposed spatiotemporal sequential turnover efficiency multi-dimensional data model tracks information in multiple dimensions throughout the entire material leasing process, ensuring that the status information of the material can be accurately and quickly reflected at any stage of leasing, thereby improving the information processing efficiency of turnover materials in the leasing process.

[0046] 2. The multi-dimensional indicator-based comprehensive evaluation method for turnover efficiency proposed in this paper extracts key indicators according to different dimensions and obtains the comprehensive evaluation result of turnover efficiency through linear weighting, providing a basis for decision-making for enterprise managers. Attached Figure Description

[0047] Figure 1 A flowchart for a multi-dimensional modeling and comprehensive evaluation method for the turnover efficiency of building materials;

[0048] Figure 2 A multi-dimensional data model diagram for spatiotemporal sequential turnover efficiency. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] The present invention provides a multi-dimensional modeling and comprehensive evaluation method for the turnover efficiency of building materials, as follows: Figure 1 As shown, the basic idea of ​​this method is to combine big data processing with energy consumption in manufacturing workshops to assist workshop managers in making decisions regarding energy conservation and emission reduction. The method includes the following steps:

[0051] Step 1, Data Modeling for Building Materials Rental:

[0052] To achieve data collection and storage, rental data modeling was first conducted. The rental process of building materials mainly includes four aspects of data: inventory data changes, real-time inventory data, project inflow and outflow data, and project balance data.

[0053] 1) Inventory data change model: Since the turnover cycle of building turnover materials is short and the inventory of turnover materials changes frequently, the inventory data change of turnover materials is the core data for improving the material turnover efficiency. Based on the basic information of the materials in the inventory, such as material number, rented quantity, remaining quantity and data change time, the inventory data change is modeled as shown in the following formula (1).

[0054] (1)

[0055] in, This indicates changes in inventory data. An ID indicating changes in inventory data. Indicates the material number. Indicates the quantity of materials rented out. This indicates the quantity of materials currently under repair. Indicates the quantity of materials lost or damaged. Indicates the remaining quantity of materials. This indicates the time period during which inventory data changes.

[0056] 2) Real-time inventory data model: Based on the changes in material flow information, the inventory data should be updated in real time so that decision-makers can understand the real-time information of the inventory materials. Therefore, a real-time inventory data model should be established based on the basic information of the materials in the inventory, such as material number, rented quantity, remaining quantity, etc., as shown in the following formula (2).

[0057] (2)

[0058] in, This represents real-time inventory data. ID representing real-time inventory data, Indicates the material number. Indicates the quantity of materials rented out. This indicates the quantity of materials currently under repair. Indicates the quantity of materials lost or damaged. Indicates the remaining quantity of materials.

[0059] 3) Project Inflow and Outflow Data Model: Construction turnover materials are used in various construction projects through leasing. In order to better understand the flow information of turnover materials and improve management efficiency, it is necessary to establish a project inflow and outflow data model based on the material usage of different projects, as shown in the following formula (3).

[0060] (3)

[0061] in, This represents the project's inflow and outflow data. This represents the ID of the project's inflow and outflow data. Indicates the project number. Indicates the material number. This indicates the total quantity of materials rented for the project on that day. This indicates the quantity of materials returned on that day. This indicates the quantity of materials still under rent at the time of project settlement on that day. This indicates the time of settlement for the project on that day.

[0062] 4) Project inventory data model: Based on the project's inflow and outflow data, the project's daily balance data should also be updated in real time so that the person in charge can understand the real-time information of the project's leased materials. Therefore, a project inventory data model should be established based on the basic information of the project's leased materials on the day, such as the project number, material number, and the total number of materials rented on the day, as shown in the following formula (4).

[0063] (4)

[0064] in, This indicates the project's remaining data. Indicates the ID of the project's remaining data. Indicates the project number. Indicates the material number. This indicates the quantity of materials being rented for the project.

[0065] Step 2, Data Reduction and Multi-Dimensional Fusion Modeling:

[0066] 1) Data reduction process:

[0067] Because the system inputs a large amount of data, the original data is prone to duplicate and erroneous data. It is necessary to reasonably reduce the obtained original data, that is, to delete duplicate data and delete erroneous data based on the reasonableness of each data content.

[0068] Based on the study of individual datasets in the system database, a raw data reduction algorithm was developed. In this algorithm, the raw lease dataset OLD is divided into the raw inventory dataset OID and the raw project dataset OPD, according to the different components of each dataset. Duplicate data is identified using material numbers and the time of data changes, and erroneous data is identified by judging the data content constraints specified in each dataset. The pseudocode for data reduction is as follows:

[0069] Input: Original inventory dataset (OID), original project dataset (OPD), data reasonableness set (RAT)

[0070] Output: Reduced dataset MCD

[0071] Algorithm flow:

[0072] For material number i

[0073] If data Satisfying the Data Reasonableness Set RAT

[0074] If data Non-empty Then

[0075] Store the data in MCD, i.e., MCD←

[0076] End if

[0077] End if

[0078] End for

[0079] For project number j

[0080] For material number k

[0081] If data Satisfying the Data Reasonableness Set RAT

[0082] If data Non-empty Then

[0083] Store the data in MCD, i.e., MCD←

[0084] End if

[0085] End if

[0086] End for

[0087] Return MCD

[0088] 2) Multi-dimensional fusion modeling

[0089] To improve the turnover efficiency of materials during the leasing process, it is necessary to track information across multiple dimensions throughout the entire leasing process, ensuring that the material's status information can be accurately and quickly reflected at any stage of the leasing. Since the material information in the model described in step 1 consists of datasets from each stage of the leasing process, the reflected material information data is complex and cannot be represented by a single dataset. Therefore, it is necessary to integrate the various datasets according to the flow logic of the materials to construct a multi-dimensional data model of turnover efficiency in a spatiotemporal sequence, such as... Figure 2 As shown.

[0090] In this model, time is used as the node to integrate the three dimensions of material information, project information and cost information, so that a dataset can cover the four dimensions of material, project, cost and time. The model is shown in the following formula (5).

[0091]

[0092] Where D represents a multi-dimensional fused dataset, This represents data information for i materials (material number, quantity rented out, and quantity remaining, etc.). This represents data information for j projects (project number, project name, materials being rented, etc.). This represents k cost data items (rental fees, transportation fees, labor costs, etc.). This represents a time point, and m is the total number of rental days.

[0093] In subsequent data analysis, specific evaluation systems and indicators can be formulated based on the multidimensional dataset provided by the model, parameters of one or two dimensions can be determined, the relationship between the changes of parameters in other dimensions can be analyzed, and corresponding evaluation results can be obtained, providing data support for the leasing decision of turnover materials.

[0094] Step 3, Key Indicator Extraction (Hierarchical Indicators):

[0095] To facilitate a comprehensive evaluation of the turnover efficiency of the entire leasing process for building materials, key indicators need to be extracted from different dimensions. The selection of indicator levels is based on the dimensions of a multi-dimensional fusion model, specifically determining the corresponding evaluation indicators through material, project, and cost dimensions.

[0096] 1) Material dimension:

[0097] The real-time rental status of a certain material directly reflects its rental value; therefore, the real-time rental rate is a key indicator for measuring the rental status of materials. Based on the material information in the dataset of real-time nodes in the multi-dimensional fusion model, the real-time rental rate of a certain material is calculated as follows:

[0098] (6)

[0099] in, This indicates the real-time rental rate of a single material. This indicates the quantity of the material rented out. This indicates the quantity of the material currently under repair. This indicates the quantity of the material lost or damaged. This indicates the remaining quantity of the material.

[0100] Since the real-time rental rate of materials only reflects the rental status of materials during that specific time period, and the demand for building materials varies at different times, the average monthly rental rate and the historical average rental rate of materials should also be calculated. The average monthly rental rate can be obtained from the material information in the multi-dimensional fusion model:

[0101] (7)

[0102] in, This indicates the average rental rate of materials in the current month. This indicates the real-time rental rate of the material, where n is the number of days in the current month.

[0103] The historical average rental rate of materials is:

[0104] (8)

[0105] in, This indicates the historical average rental rate of materials. This indicates the real-time rental rate of materials, where p is the number of days the materials have been rented.

[0106] 2) Project Dimension:

[0107] At the project level, the ratio of the quantity of materials rented for a particular project to the total quantity of materials rented for that project reflects the project's dependence on that material. This is a crucial indicator of the relationship between materials and projects, facilitating targeted material preparation by the lessor. Based on the material information in the real-time node dataset of the multi-dimensional fusion model, the real-time proportion of a certain material in the project is calculated as follows:

[0108] (9)

[0109] in, This indicates the real-time percentage of a certain material in the i-th project. This indicates the total quantity of materials rented for the project on that day. This indicates the quantity of the material that has been rented out.

[0110] Since the real-time percentage of a certain material in a project only reflects the rental situation of that material during that period, similarly, given the different demands for building materials at different times, the monthly rental percentage of a certain material in the project should also be calculated:

[0111] (10)

[0112] in, This represents the percentage of a certain material rented in the i-th project in that month. This represents the percentage of a certain material rented in the i-th project in a given month, where n is the number of days in that month.

[0113] For projects with long durations, material rental periods vary significantly. Simply recording the rental percentage for one month is insufficient to clearly reflect the overall material usage in the project. It is also necessary to calculate the historical rental percentage of a particular material within the project.

[0114] (11)

[0115] in, This represents the historical rental percentage of a certain material in the i-th project. q represents the real-time percentage of a certain material in the i-th project, and q is the number of days the project rents the certain material.

[0116] Construction projects typically rent a wide variety of materials. The average percentage of total materials rented in a project can reflect its material needs, allowing the lessor to prepare materials in advance and improve turnover efficiency. Furthermore, the higher the percentage of total materials rented, the greater the project's revenue contribution to the lessor. Therefore, the historical percentage of all materials rented in a project should be calculated.

[0117] (12)

[0118] in, This represents the historical percentage of all materials rented for the i-th project. This represents the historical rental percentage of a certain material in the i-th project, where m is the number of types of materials rented in the project.

[0119] 3) Cost perspective

[0120] The evaluation indicators mentioned above are based on the rental volume. In order to improve turnover efficiency and increase rental revenue, it is also necessary to provide corresponding evaluation indicators from the perspective of costs.

[0121] The total rental costs for building materials mainly include rental fees, transportation fees, ironwork fees, labor costs, maintenance fees, entry fees, exit fees, wall support and joint raising fees, contracting fees, etc. The corresponding costs vary for different projects and different materials. Therefore, the monthly accounting entries for different materials and different projects should be calculated based on the lessor's fee settlement cycle (usually one month).

[0122] The monthly accounting entries for different materials are as follows:

[0123] (13)

[0124] in, This indicates the monthly accounting entry for the k-th material, where m represents the number of categories of rental fees for that material. This represents the rental cost for a certain type of material of the kth type.

[0125] The monthly revenue details for different projects are as follows:

[0126] (14)

[0127] in, This indicates the monthly revenue recorded for the k-th project, where n represents the number of types of materials rented for that project, and m represents the number of categories of rental fees for each type of material. This represents the rental cost for a certain type of material of the kth type.

[0128] Based on the determination of the indicators from the above dimensions, the indicators can be summarized as shown in Table 1:

[0129] Table 1 Multi-dimensional analysis indicators of building turnover materials

[0130]

[0131] Step 4, Comprehensive evaluation method for turnover efficiency based on multi-dimensional indicators:

[0132] Combining the aforementioned multi-dimensional indicators of building turnover materials, a comprehensive evaluation method for turnover efficiency can be formulated using a linear weighted method. Based on the indicators of each dimension, the following evaluation function is constructed:

[0133] (15)

[0134] in, This represents the calculated comprehensive index. Let represent the i-th analytical indicator function, and r represent the number of indicators considered by the decision-maker. ≥0 (i=1,2,3,…,m) are weight coefficients, and

[0135] (16)

[0136] A comprehensive turnover efficiency evaluation method can help companies analyze various evaluation results, such as the optimal inventory level for material preparation, the best cooperative target projects, and the maximum rental revenue for the company. Therefore, based on the company's required evaluation objectives, the weights of each objective value in the function can be reasonably allocated to obtain the comprehensive evaluation result of the company's required turnover efficiency, providing a basis for decision-making for company managers.

Claims

1. A method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building materials, characterized in that, Includes the following steps: Step 1, Data Modeling for Building Materials Rental: Based on the basic information of the materials in the inventory, we establish inventory data change models and real-time inventory data models respectively for inventory data change and real-time inventory data. We also establish project inflow and outflow data models based on the materials used in different projects, and project inventory data models based on the basic information of the materials leased by the project on the same day. Step 2, Data Reduction and Multi-Dimensional Fusion Modeling: First, the rental data for building materials was simplified by removing duplicate data and deleting erroneous data based on the reasonableness of each data entry. Secondly, based on the circulation logic of turnover materials, the datasets are integrated to construct a multi-dimensional data model of turnover efficiency in a spatiotemporal sequence. The multi-dimensional data model is a fusion model that uses time as a node and integrates three dimensions: material information, project information, and cost information. This allows a single dataset to encompass four dimensions: materials, project, cost, and time. The expression is as follows: （5） Where D represents a multi-dimensional fused dataset, This represents i pieces of material data. This represents data information for j projects. This represents k cost data points. This indicates a time point, where m is the total number of rental days; Step 3, Key Indicator Extraction: The selection of indicator levels is based on the dimensions of the multi-dimensional data model. Corresponding evaluation or assessment indicators are determined through material, project, and cost dimensions, including: 1) Material-level evaluation metrics: Real-time rental rate of a certain material. （6） ， in, This indicates the real-time rental rate of a single material. This indicates the quantity of the material rented out. This indicates the quantity of the material currently under repair. This indicates the quantity of the material lost or damaged. Indicates the remaining quantity of the material; 2) Project-level evaluation indicators: the real-time proportion of a certain material in the project. （9） ， in, This indicates the real-time percentage of a certain material in the i-th project. This indicates the total quantity of materials rented for the project on that day. This indicates the quantity of the material rented out; 3) Cost-related evaluation indicators: Monthly accounting entries for different materials and different projects. The monthly accounting entries for different materials are as follows: （13） ， in, This indicates the monthly accounting entry for the k-th material, where m represents the number of categories of rental fees for that material. This represents the rental cost for a certain type of material; The monthly revenue details for different projects are as follows: （14） ， in, This indicates the monthly revenue recorded for the k-th project, where n represents the number of types of materials rented for that project, and m represents the number of categories of rental fees for each type of material. This represents the rental cost for a certain type of material; Step 4, Comprehensive evaluation of turnover efficiency based on multi-dimensional indicators: Combining multi-dimensional evaluation indicators of building turnover materials, a comprehensive evaluation method for turnover efficiency is formulated using a linear weighting method. Based on the indicators of each dimension, an evaluation function is constructed. （15） ， in, This represents the calculated comprehensive index. Let represent the i-th analytical indicator function, and r represent the number of indicators considered by the decision-maker. ≥0 (i=1,2,3,…,m) are weight coefficients, and （16） ， By analyzing the various evaluation results through a comprehensive turnover efficiency evaluation method, and based on the required evaluation objectives, the weights of each objective value in the function are reasonably allocated to obtain the required comprehensive turnover efficiency evaluation result.

2. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 1, characterized in that, The data reduction process in step 2 involves studying individual datasets in the system database and developing an original data reduction algorithm. In the algorithm, based on the different constituent data of an individual dataset, the original lease dataset OLD is divided into the original inventory dataset OID and the original project dataset OPD. Duplicate data is identified by using the material number and the time of data change, and erroneous data is identified by judging the data content constraints specified in the individual dataset.

3. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 1, characterized in that, The material-level evaluation indicators in step 3 also include the average monthly rental rate of a certain material: （7） ， in, This indicates the average rental rate of materials in the current month. This indicates the real-time rental rate of the material, where n is the number of days in the current month.

4. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 3, characterized in that, The material-level evaluation metrics in step 3 also include the historical average rental rate of a certain material: （8） ， in, This indicates the historical average rental rate of materials. This indicates the real-time rental rate of materials, where p is the number of days the materials have been rented.

5. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 1, characterized in that, The project-level evaluation indicators in step 3 also include the percentage of a certain material rented in the project during the current month: （10） ， in, This represents the percentage of a certain material rented in the i-th project in that month. This represents the percentage of a certain material rented in the i-th project in a given month, where n is the number of days in that month.

6. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 5, characterized in that, The project-level evaluation indicators in step 3 also include the historical rental percentage of a certain material in the project: （11） ， in, This represents the historical rental percentage of a certain material in the i-th project. q represents the real-time percentage of a certain material in the i-th project, and q is the number of days the project rents the certain material.

7. The method for multi-dimensional modeling and comprehensive evaluation of the turnover efficiency of building turnover materials according to claim 6, characterized in that, The project-level evaluation indicators in step 3 also include the historical rental percentage of all materials rented by the project: （12） ， in, This represents the historical percentage of all materials rented for the i-th project. This represents the historical rental percentage of a certain material in the i-th project, where m is the number of types of materials rented in the project.

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