Engineering cost method and system based on big data

Abstract

The invention relates to the technical field of engineering cost, and provides an engineering cost method and system based on big data, and the method comprises the steps: recognizing the big data related to an engineering project in a database, carrying out the targeted analysis and processing of the data, obtaining the building material data related to the engineering cost, and storing the building material data in a database; and the engineering cost data is corrected, so that the prediction and evaluation of the engineering cost are realized, errors and deviations caused by human factors are reduced, the real-time performance and accuracy of the engineering cost are ensured, and the risks of project cost hyper-branched and out-of-control are reduced.

Description

Technical Field

[0001] The present invention relates to the field of engineering cost technology, and in particular to an engineering cost method and system based on big data. Background Art

[0002] As the scale of construction projects continues to expand and their complexity increases, project cost is becoming increasingly important for project management and cost control. Project cost is generally calculated before the project is implemented and used to guide subsequent projects. However, the construction market and actual construction conditions are changing rapidly, and traditional project cost methods can no longer meet the needs of modern engineering projects for cost control and efficiency improvement. Summary of the invention

[0003] The present invention provides an engineering cost method based on big data, which is used to solve the problem that engineering cost in the prior art is not timely and accurate enough.

[0004] The first aspect of the present invention provides a construction cost method based on big data, comprising:

[0005] Acquire engineering project data, identify building materials and project information required in the engineering project data, wherein the project information includes project area, project enterprise and project type; acquire price data of each building material belonging to the current project area in the database to obtain first building material data; acquire surplus ratio of each material under each project type belonging to the current project area in the database to obtain second building material data; acquire price data of each building material in the current area under the current project enterprise, and surplus ratio of materials of each project type in each area in the database to obtain third building material data and fourth building material data;

[0006] The project cost is calculated based on the project data and revised based on the data of various building materials.

[0007] Optionally, the project cost is calculated based on the project data, and the project cost is corrected based on the data of each building material, specifically:

[0008] The first building material data is divided into preset time intervals, the first building material price characteristic value in each time interval is calculated, and the first characteristic value trend function is obtained by fitting the discrete building price characteristic value; the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval is calculated to obtain the difference ratio characteristic value; the characteristic values ​​of the second building material data and the fourth building material data are calculated respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value;

[0009] Substitute the trend function and each eigenvalue into the preset cost fluctuation model, obtain the quotation cycle and generate the engineering cost data based on the cost fluctuation model. The cost fluctuation model is specifically:

[0010]

[0011] Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ n is the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

[0012] Optionally, after the third building material data and the fourth building material data, the following further includes:

[0013] The data of each building material is cleaned and the building material data with data value differences greater than the preset threshold are eliminated.

[0014] The second aspect of the present application provides an engineering cost system based on big data, including:

[0015] The data acquisition module is used to acquire engineering project data, identify the building materials and project information required in the engineering project data, wherein the project information includes the project area, project enterprise and project type; acquire the price data of each building material belonging to the current project area in the database to obtain the first building material data; acquire the surplus ratio of each material under each project type belonging to the current project area in the database to obtain the second building material data; acquire the price data of each building material in the current area under the current project enterprise, and the surplus ratio of the material of each project type in each area in the database to obtain the third building material data and the fourth building material data;

[0016] The data processing and correction module is used to calculate the project cost based on the project data and to correct the project cost based on the data of various building materials.

[0017] Optionally, in the data processing and correction module, the construction cost is calculated according to the project data, and the construction cost is corrected according to the data of each building material, specifically:

[0018] The first building material data is divided into preset time intervals, the first building material price characteristic value in each time interval is calculated, and the first characteristic value trend function is obtained by fitting the discrete building price characteristic value; the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval is calculated to obtain the difference ratio characteristic value; the characteristic values ​​of the second building material data and the fourth building material data are calculated respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value;

[0019] Substitute the trend function and each eigenvalue into the preset cost fluctuation model, obtain the quotation cycle and generate the engineering cost data based on the cost fluctuation model. The cost fluctuation model is specifically:

[0020]

[0021] Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ n is the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

[0022] Optionally, in the data acquisition module, after the third building material data and the fourth building material data, further includes:

[0023] The data of each building material is cleaned and the building material data with data value differences greater than the preset threshold are eliminated.

[0024] The third aspect of the present application provides a method and device for engineering cost estimation based on big data, the device comprising a processor and a memory:

[0025] The memory is used to store program code and transmit the program code to the processor;

[0026] The processor is used to execute a big data-based engineering cost estimation method as described in any one of the first aspects of the present invention according to the instructions in the program code.

[0027] The fourth aspect of the present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the engineering cost method based on big data as described in any one of the first aspects of the present invention.

[0028] It can be seen from the above technical scheme that the present invention has the following advantages: by identifying big data related to engineering projects in the database and conducting targeted data analysis and processing, building material data related to engineering costs are obtained, and the engineering cost data is corrected, thereby realizing the prediction and evaluation of engineering costs, reducing errors and deviations caused by human factors, ensuring the real-time and accuracy of engineering costs, and reducing the risks of project cost overruns and loss of control. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 This is a flowchart of a construction cost method based on big data;

[0031] Figure 2 This is a structural diagram of an engineering cost system based on big data. DETAILED DESCRIPTION

[0032] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] The present invention provides an engineering cost method based on big data, which is used to solve the problem that engineering cost in the prior art is not timely and accurate enough.

[0034] See also Figure 1 , Figure 1 The first flow chart of a construction cost estimation method based on big data provided by an embodiment of the present invention.

[0035] S100, obtaining engineering project data, identifying building materials and project information required in the engineering project data, wherein the project information includes project area, project enterprise and project type; obtaining price data of various building materials belonging to the current project area from a database to obtain first building material data; obtaining surplus ratios of various materials under various project types belonging to the current project area from a database to obtain second building material data; obtaining price data of various building materials in the current area under the current project enterprise, and surplus ratios of materials of various project types in each area from a database to obtain third building material data and fourth building material data;

[0036] It should be noted that each engineering project will have its own list of bidding documents, and the specific content may include engineering construction design drawings or bills of quantities. The engineering project data can be obtained based on the contents of these documents, and the construction materials needed for the construction of items such as square piles and walls in the current engineering project can be obtained. Construction materials such as steel bars, bricks, commercial concrete and cables, etc. The project information can be used to obtain the amount of required construction materials from the length, area or volume of the building; the project information can also include information about the company contracting the current project, which is convenient for subsequent identification of data references of related companies in the database; and the project type can be new construction, expansion, reconstruction, restoration, etc., and the specific projects to be constructed can be divided into beams, columns, floor slabs or walls to divide the project types.

[0037] A database can be constructed based on the market price information and material consumption cases disclosed on the bidding information website of the project area or the regional housing and construction information management. Due to the different material output and demand in different regions, as well as different transportation costs, the prices of different construction materials will vary from region to region. For example, the price of sediment is lower in coastal and riverside areas, and the price of steel is lower in mining areas. In addition, as the months change and the market develops, prices will also change over time. Based on the construction materials required by the current engineering project, the price fluctuation data of various construction materials in the project area over a period of time is obtained. The price fluctuation at each time point constitutes one data, and the first construction material data obtained is multiple; the loss of construction materials in different regions is also different. For example, the loss of concrete in arid inland areas during construction and transportation will be greater, and the same construction material will be used when building different types of projects. The average loss at different times is also different, so the material surplus ratio can be obtained based on the same project type and project area; in the bill of quantities of the engineering project, there will be the amount of building materials required for a certain project. For example, a load-bearing beam requires A cubic meters of concrete. Due to construction losses, transportation losses and other reasons, a saturated building material procurement volume is actually required to avoid insufficient materials during construction. More materials need to be purchased than the list in the design drawings. After the project is completed, the excess building materials can be transferred to other engineering projects or sold externally, and the excess building materials will not be wasted. Therefore, it is only necessary to calculate the ratio of (BA) / A based on the actual building material usage B cubic meters recorded in the database to obtain the material surplus ratio of multiple building materials. The material surplus ratio reflects the actual usage characteristics of building materials for a certain project type in a region;

[0038] When performing engineering cost analysis and calculation for the current project enterprise, the historical engineering project records within the current project enterprise can be imported into the database, which will include the price data of various building materials that the current enterprise can obtain from suppliers. Due to different channels and strengths of different enterprises, the prices of the same building materials in the same region may also be different. The third building material data reflects the prices of various building materials that the current project enterprise can obtain in the current project area. The third building material data can also be obtained by screening the data belonging to the current project enterprise in the first building material data; the second building material data reflects the characteristics of regional material surpluses and reduces the impact of enterprise specificity. The specifications in engineering construction require a safety standard range. During construction, it may be closer to the upper limit of the range or to the lower limit of the range, which all meet the requirements. However, different institutional requirements within the enterprise will result in large differences in material surpluses between different enterprises. Then, by excluding regional characteristics, the material surplus ratio is calculated for the current enterprise's building material surpluses under various project types in various regions. The fourth building material data obtained reflects the enterprise characteristics, and the fourth building material can include the same enterprise data in multiple regions. The standards for construction in different locations of the same enterprise should be consistent.

[0039] S200, calculating the construction cost according to the construction project data, and revising the construction cost according to the data of each building material.

[0040] It should be noted that the project data includes the required types of building materials and the quantities of each material. The price of each building material can be simply set based on the pricing benchmark issued by the industry construction authority. This pricing is only a reference price and is still different from the actual price. The benchmark project cost is obtained by multiplying the quantity of each building material by the reference price and then summing them up. The reference price is then corrected by the first building material data and the third building material data, and the quantity of building materials is corrected by the second building material data and the fourth building material data to obtain a more accurate project cost.

[0041] In this embodiment, by identifying big data related to the engineering project in the database and performing targeted data analysis and processing, the building material data related to the engineering cost is obtained, and the engineering cost data is corrected, thereby realizing the prediction and evaluation of the engineering cost, reducing the errors and deviations caused by human factors, ensuring the real-time and accuracy of the engineering cost, and reducing the risk of project cost overruns and loss of control.

[0042] The above is a detailed description of the first embodiment of a construction cost method based on big data provided by the present application. The following is a detailed description of the second embodiment of a construction cost method based on big data provided by the present application.

[0043] In this embodiment, a method for calculating the construction cost based on big data is further provided. In the aforementioned step S200, the construction cost is calculated according to the construction project data, and the construction cost is corrected according to the data of each building material. Specifically, steps S201-S202 are also included:

[0044] S201, dividing the first building material data according to preset time intervals, calculating the first building material price characteristic value in each time interval, and fitting the first characteristic value trend function according to the discrete building price characteristic value; calculating the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval to obtain the difference ratio characteristic value; calculating the characteristic values ​​of the second building material data and the fourth building material data respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value;

[0045] It should be noted that a preset time interval can be set according to the frequency of material price fluctuations in various regions, such as 3 days, 7 days or one month, and the first building material data belonging to the same price range can be statistically analyzed; the material price characteristic value cannot simply adopt the average value of the total price divided by the number of projects, because bulk materials will fluctuate due to the purchase quantity. The larger the quantity, the lower the channel price can be. Therefore, it is necessary to calculate the average value based on the material purchase quantity of each project. For example, within a certain time interval, the first building material data of a project is a, and the purchase quantity is A. The first building material data of another project is b, and the purchase quantity is B. Then (aA+bB) / (A+B) is calculated to obtain the first building material price characteristic value, which is the average price of building materials in the current time interval in the current region; each time interval has a corresponding first building material price characteristic value, and each first building material price characteristic value is discrete. The first characteristic value trend function can be obtained based on polynomial fitting or least squares fitting, which is a function of the average price of building materials in the current region changing over time;

[0046] The first building material data reflects the overall building material price in the region, and the overall building material price of each enterprise will fluctuate up and down due to different channels and strengths. Therefore, the difference ratio characteristic value obtained by calculating the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval can be used to reflect the fluctuation of the material price obtained by the current enterprise relative to the regional price. For example, in the T time interval corresponding to a certain month in a certain year, the first building material price characteristic value is A, and the third building material data is B, then the difference ratio is (BA) / A. The average of the difference ratios in multiple time intervals is taken to obtain the difference ratio characteristic value, which reflects the relationship between the current building material price obtained by the enterprise and the regional average.

[0047] Because the overall construction process under the standardization of the construction industry is similar, the second building material data of the same project type and the same building material is calculated by averaging to obtain the regional surplus characteristic value, which can reflect the current regional average material surplus ratio characteristics; and the company's construction process also has its own unified standards internally, so the fourth building material data of the same project type and the same building material can also be calculated by averaging to obtain the company surplus characteristic value, which can reflect the current corporate average material surplus ratio characteristics. Furthermore, seasonal climate reasons in the region may lead to different losses of different building materials, such as building material losses caused by rainy seasons and dry seasons. Therefore, the second building material data can be divided into time intervals, and the discrete second building material data can be fitted to obtain the second characteristic value trend function, and the prediction function of the regional material surplus over time can be obtained, which can replace the regional surplus characteristic value in subsequent steps.

[0048] S202, substituting the trend function and each characteristic value into a preset cost fluctuation model, obtaining the quotation cycle and generating engineering cost data based on the cost fluctuation model, wherein the cost fluctuation model is specifically:

[0049]

[0050] Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ n is the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

[0051] It should be noted that a project needs to be constructed by multiple types of building materials. Therefore, when calculating the project cost, it is necessary to collect the fluctuation characteristics of each building material. In the cost fluctuation model, each building material will be corrected based on the characteristic value calculated in the above step S201, and then multiplied by the price corresponding to the estimated quotation cycle and summed. The demand for the nth type of building material is the theoretical demand quantity of the building material in the construction design drawing or the bill of quantities, that is, the amount of material without loss; when preparing or bidding for a project, it is necessary to have project cost data as a reference, but the market fluctuations during the final procurement and construction will cause changes in the project cost. Therefore, a quotation cycle can be obtained according to the estimated project landing time, and the quotation cycle can be substituted into the cost fluctuation model to predict a more accurate project cost when it is finally landed; although the current strategy in the project settlement will be agreed upon according to the contract, if the price of the purchased materials exceeds a certain percentage of the benchmark price, it needs to be adjusted according to the purchase price, but being able to estimate and analyze the cost in advance can gain more advantages in the competition in the construction market.

[0052] Furthermore, after the third building material data and the fourth building material data in the aforementioned step S100, it also includes: cleaning each building material data, and eliminating building material data whose data value difference is greater than a preset threshold. It should be noted that after filtering and acquiring the big data in the database, it is also necessary to avoid individual differences leading to deviations in the final overall data. Therefore, data cleaning should be performed based on each building material data, and building material data with large differences from the overall data should be eliminated. The preset threshold can be set according to a certain proportion of the parameter average value, and the specific proportion can be set according to the experience value. For example, in a certain engineering project, due to quality problems, multiple constructions have resulted in the proportion of residual materials of building materials far exceeding the average value. Such data needs to be eliminated to avoid contaminating the big data.

[0053] The above is a detailed description of an engineering cost method based on big data provided in the first aspect of the present application. The following is a detailed description of an embodiment of an engineering cost system based on big data provided in the second aspect of the present application.

[0054] See also Figure 2 , Figure 2 This embodiment provides a construction cost system based on big data, including:

[0055] The data acquisition module 10 is used to acquire engineering project data, identify the building materials and project information required in the engineering project data, wherein the project information includes the project area, project enterprise and project type; acquire the price data of each building material belonging to the current project area in the database to obtain the first building material data; acquire the surplus ratio of each material under each project type belonging to the current project area in the database to obtain the second building material data; acquire the price data of each building material in the current area under the current project enterprise, and the surplus ratio of the material of each project type in each area in the database to obtain the third building material data and the fourth building material data;

[0056] The data processing and correction module 20 is used to calculate the construction cost according to the construction project data and to correct the construction cost according to the data of each building material.

[0057] Furthermore, in the data processing and correction module 20, the construction cost is calculated according to the project data, and the construction cost is corrected according to the data of each building material, specifically:

[0058] The first building material data is divided into preset time intervals, the first building material price characteristic value in each time interval is calculated, and the first characteristic value trend function is obtained by fitting the discrete building price characteristic value; the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval is calculated to obtain the difference ratio characteristic value; the characteristic values ​​of the second building material data and the fourth building material data are calculated respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value;

[0059] Substitute the trend function and each eigenvalue into the preset cost fluctuation model, obtain the quotation cycle and generate the engineering cost data based on the cost fluctuation model. The cost fluctuation model is specifically:

[0060]

[0061] Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ nis the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

[0062] Furthermore, in the data acquisition module 10, after the third building material data and the fourth building material data, the following further comprises:

[0063] Clean the data of each building material and eliminate the building material data whose data value difference is greater than a preset threshold. The third aspect of the present application also provides a construction cost method device based on big data, including a processor and a memory: the memory is used to store program code and transfer the program code to the processor; the processor is used to execute the above-mentioned construction cost method based on big data according to the instructions in the program code.

[0064] A fourth aspect of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store program code, and the program code is used to execute the above-mentioned engineering cost method based on big data.

[0065] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0066] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0067] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0070] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of engineering cost estimation based on big data, Features include: Acquire engineering project data, identify required building materials and project information in the engineering project data, wherein the project information includes project area, project enterprise and project type; Obtain the price data of each building material belonging to the current project area in the database to obtain the first building material data; obtain the surplus ratio of each material under each project type belonging to the current project area in the database to obtain the second building material data; obtain the price data of each building material in the current area under the current project enterprise and the surplus ratio of the material of each project type in each area in the database to obtain the third building material data and the fourth building material data; The project cost is calculated based on the project data and revised based on the data of various building materials.

2. According to the engineering cost method based on big data as described in claim 1, It is characterized in that The project cost is calculated based on the project data, and the project cost is corrected based on the data of each building material, specifically: The first building material data is divided into preset time intervals, the first building material price characteristic value in each time interval is calculated, and the first characteristic value trend function is obtained by fitting the discrete building price characteristic value; the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval is calculated to obtain the difference ratio characteristic value; Calculate the characteristic values ​​of the second building material data and the fourth building material data respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value; Substitute the trend function and each eigenvalue into the preset cost fluctuation model, obtain the quotation cycle and generate the project cost data based on the cost fluctuation model. The cost fluctuation model is specifically: Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ n is the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

3. According to the engineering cost method based on big data as described in claim 1, It is characterized in that After the third building material data and the fourth building material data, it also includes: The data of each building material is cleaned and the building material data with data value differences greater than the preset threshold are eliminated.

4. A construction cost system based on big data, It is characterized in that include: The data acquisition module is used to acquire engineering project data, identify the building materials and project information required in the engineering project data, wherein the project information includes the project area, project enterprise and project type; acquire the price data of each building material belonging to the current project area in the database to obtain the first building material data; acquire the surplus ratio of each material under each project type belonging to the current project area in the database to obtain the second building material data; acquire the price data of each building material in the current area under the current project enterprise, and the surplus ratio of the material of each project type in each area in the database to obtain the third building material data and the fourth building material data; The data processing and correction module is used to calculate the construction cost based on the project data and to correct the construction cost based on the data of various building materials.

5. According to the big data-based engineering cost system of claim 4, It is characterized in that In the data processing and correction module, the construction cost is calculated according to the project data, and the construction cost is corrected according to the data of each building material, specifically: The first building material data is divided into preset time intervals, the first building material price characteristic value in each time interval is calculated, and the first characteristic value trend function is obtained by fitting the discrete building price characteristic value; the difference ratio between the third building material data and the corresponding first building material price characteristic value in the same time interval is calculated to obtain the difference ratio characteristic value; Calculate the characteristic values ​​of the second building material data and the fourth building material data respectively to obtain the regional surplus characteristic value and the enterprise surplus characteristic value; Substitute the trend function and each eigenvalue into the preset cost fluctuation model, obtain the quotation cycle and generate the project cost data based on the cost fluctuation model. The cost fluctuation model is specifically: Among them, P is the engineering cost data, μ is the difference ratio characteristic value, and f n (t) is the first eigenvalue trend function, a is the number of building material types in the project, γ n is the regional surplus characteristic value of the nth type of building materials, δ n is the enterprise surplus characteristic value of the nth type of building materials, s n is the demand for the nth type of building materials, and t is the quotation cycle.

6. According to the big data-based engineering cost system of claim 4, It is characterized in that In the data acquisition module, after the third building material data and the fourth building material data, the following further comprises: The data of each building material is cleaned and the building material data with data value differences greater than the preset threshold are eliminated.

7. A construction cost equipment based on big data, It is characterized in that The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute a big data-based engineering cost method as described in any one of claims 1 to 3 according to the instructions in the program code.

8. A computer-readable storage medium, It is characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the engineering cost method based on big data as described in any one of claims 1-3.

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