Engineering consultation management system based on big data

Abstract

The invention relates to the technical field of constructional engineering informatization, and discloses an engineering consultation management system based on big data. Collecting data of material declaration price, market average price, project submission amount, project review amount, change amount cumulant, contract initial amount, project payment paid proportion and project actual completion progress in project cost through access of multi-source heterogeneous data; and the data processing module is used for processing and analyzing the data acquired by the data acquisition module so as to calculate the deviation condition of the material unit price, the deviation condition of the engineering quantity, the density condition of the change signature and the payment deviation condition of the progress money. According to the method, comprehensive collection of multi-source heterogeneous data is adopted, the problem that a traditional system is single in data source is solved, the information coverage range is widened, and false or missing information in the project cost is rapidly judged through an integrated calculation engine.

Description

Technical Field

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

[0002] Engineering cost consulting refers to the provision of intellectual services in the decision-making and implementation management of engineering construction projects for government departments, project owners and other types of clients, following the principles of independence, science and impartiality, and applying knowledge and experience in multiple disciplines such as engineering technology, science and technology, economic management, laws and regulations. This includes consulting activities in the early project establishment stage, consulting in the survey and design stage, consulting in the construction stage, and evaluation after commissioning or delivery.

[0003] An existing patent discloses a big data-based engineering cost consulting management system and method (publication number CN119693075A), belonging to the field of engineering cost consulting management technology. The system includes a data acquisition module, a data storage module, a data analysis module, a project cost estimation module, a cost risk warning module, a project cost optimization module, and a project cost tracking module. In the technology disclosed in this patent, abnormalities in engineering projects are still determined by traditionally determining whether a single review indicator exceeds a threshold. However, in reality, due to the vast amount of engineering cost data, multiple data indicators in materials and contracts are often intentionally kept below the threshold to circumvent review risks and pursue personal gain. Summary of the Invention

[0004] In order to solve existing technical problems, the present invention provides an engineering consulting management system based on big data, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, an engineering consulting management system based on big data, comprising: The data collection module collects data on project costs, including material declared prices, market average prices, project review quantities, project review quantities, cumulative change amounts, initial contract amounts, proportion of project payments paid, and actual project completion progress, by accessing multi-source heterogeneous data. The data processing module is used to process and analyze the data collected by the data acquisition module, and then calculate the deviation of material unit price, deviation of engineering quantity, density of change signatures and deviation of progress payment; The intelligent analysis module is used to receive the calculation data fed back by the data processing module, and use the integrated calculation engine to determine whether there is any false or missing information in the project cost.

[0006] Furthermore, the engineering consulting management system based on big data also includes a data storage module; The data storage module is used to construct a storage system that combines multi-source heterogeneous data and non-multi-source heterogeneous data.

[0007] Furthermore, the data acquisition module also performs preliminary data processing near the data generation source through the edge computing node.

[0008] Furthermore, the data information fed back by the integrated calculation engine data processing module in the intelligent analysis module is used to calculate the condition coefficient for the existence of false or missing information in the project cost, so:

[0009] Where, A conditional coefficient indicating the presence of false or missing information in the project cost; Indicates the comprehensive deviation of engineering material price and quantity; represents the change signature density index; Indicates the progress payment deviation rate of the contract.

[0010] Furthermore, the intelligent analysis module determines the comprehensive deviation of the price and quantity of engineering materials according to the deviation of the unit price of materials and the deviation of the engineering quantity. , then we have:

[0011] Where, Represents an adjustable constant used to control the rate of change of the integrated deviation and determine the sensitivity of the model; Indicates the impact of the deviation of the material unit price on the comprehensive deviation; Indicates the deviation rate of material unit price; Indicates the influence of the deviation of engineering quantity on the comprehensive deviation; Indicates the deviation rate of engineering quantity.

[0012] Furthermore, when When , it means that the analysis results of the engineering cost data collected in this consultation show that there is false or missing information; when , it means that the analysis results of the engineering cost data collected in this consultation show that there is no false or missing information.

[0013] Furthermore, the intelligent analysis module also uses multimodal analysis to identify possible false or missing information in the project cost, and then uses dynamic models to optimize adjustable constants. value.

[0014] Furthermore, the intelligent analysis module directly accesses structured and unstructured data from different devices or systems, analyzes and provides conditional coefficients that can determine whether there is false or missing information in the project cost. threshold.

[0015] The present invention provides an engineering consulting management system based on big data. Compared with the existing technology, this method has the following effects: 1. This invention solves the problem of single data source in traditional systems by adopting comprehensive collection of multi-source heterogeneous data, improves information coverage, and quickly determines false or missing information in project costs through an integrated calculation engine, reducing manual review time and improving the response speed of engineering consultation.

[0016] 2. The present invention performs preliminary processing at the source of the data, and the hybrid storage system reduces data redundancy and improves storage efficiency. It also reduces data transmission pressure and improves real-time processing capabilities through edge computing. In addition, local data processing shortens the analysis cycle, which is particularly suitable for dynamic monitoring of large-scale engineering projects.

[0017] 3. The present invention quantitatively analyzes parameters such as material price deviation and engineering quantity deviation through mathematical formulas, and combines them with dynamic adjustment parameters, so that the system can provide early warning of false or missing information in engineering costs and reduce engineering audit risks.

[0018] 4. The present invention dynamically adjusts constants through intelligent analysis modules This can effectively solve the problem of deliberately keeping multiple data indicators in materials and contracts below the threshold to avoid review risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the structure of the engineering consulting management system for big data in the present invention; Figure 2 is a relationship diagram between the conditional coefficient G and a and c in the present invention; Figure 3 For the present invention Impact on the comprehensive deviation a. DETAILED DESCRIPTION

[0020] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 like Figure 1 According to one aspect of the present invention, there is provided an engineering consulting management system based on big data, comprising: The data acquisition module, through access to multi-source heterogeneous data, collects data on project costs, including material declared prices, average market prices, project submissions, project reviews, cumulative change amounts, initial contract amounts, percentage of project payments paid, and actual project completion progress. The data processing module processes and analyzes the data collected by the data acquisition module, calculating deviations in material unit prices, project quantity, change signature density, and progress payment deviations. The intelligent analysis module receives the calculated data fed back by the data processing module and uses an integrated calculation engine to determine whether any information in the project cost is false or missing. Comprehensive collection of multi-source heterogeneous data (such as material prices, contract amounts, and project progress) addresses the single data source issue of traditional systems, improves information coverage, and rapidly identifies false or missing information in the project cost through the integrated calculation engine, reducing manual review time and improving the response speed of engineering consultations.

[0022] Example 2 like Figure 1 As shown, the big data-based engineering consulting and management system also includes a data storage module; the data storage module is used to build a storage system that combines multi-source heterogeneous data with non-multi-source heterogeneous data. The data acquisition module also uses edge computing nodes to perform preliminary data processing near the data generation source. The introduction of the data storage module and edge computing nodes supports the mixed storage of multi-source heterogeneous data and non-multi-source heterogeneous data, and performs preliminary processing at the data source. The hybrid storage system reduces data redundancy and improves storage efficiency. Edge computing reduces data transmission pressure and enhances real-time processing capabilities. Moreover, local data processing shortens the analysis cycle, making it particularly suitable for the dynamic monitoring of large-scale engineering projects.

[0023] Example 3 like Figure 1-3 As shown, the intelligent analysis module determines the comprehensive deviation of engineering material price and quantity based on the deviation of material unit price and engineering quantity. , then we have:

[0024] Where, Represents an adjustable constant used to control the rate of change of the integrated deviation and determine the sensitivity of the model; Indicates the impact of the deviation of the material unit price on the comprehensive deviation; Indicates the deviation rate of material unit price; Indicates the influence of the deviation of engineering quantity on the comprehensive deviation; Indicates the deviation rate of engineering quantity.

[0025] like Figure 2Indicates the fixed payment deviation rate m = 9%, the comprehensive deviation between G and the price and quantity of engineering materials and change signature density index The data information fed back by the integrated calculation engine data processing module in the intelligent analysis module is used to calculate the condition coefficient of false or missing information in the project cost, then:

[0026] Where, A conditional coefficient indicating the presence of false or missing information in the project cost; Indicates the comprehensive deviation of engineering material price and quantity; represents the change signature density index; Indicates the progress payment deviation rate of the contract.

[0027] Among them, the comprehensive deviation of engineering material prices and quantities The influence of the deviation of material unit price on the comprehensive deviation is calculated. (The impact of the deviation of material unit price on the comprehensive deviation amount = the number of samples for calculating the deviation rate of material unit price / (the number of samples for calculating the deviation rate of material unit price + the number of samples for calculating the deviation rate of engineering quantity)). (The deviation rate of material unit price = (declared unit price - average market price during the same period) / average market price × 100%. Among them, when the deviation rate of material unit price is greater than 15%, it means that it exceeds the risk threshold in normal review).

[0028] The influence of the deviation of engineering quantity on the comprehensive deviation is (The influence of the deviation of the engineering quantity on the comprehensive deviation = the number of samples for calculating the deviation rate of engineering quantity / (the number of samples for calculating the deviation rate of material unit price + the number of samples for calculating the deviation rate of engineering quantity)). ((Quantity submitted for review - quantity reviewed) / quantity reviewed × 100%. Among them, when the deviation rate of the engineering quantity is greater than 25%, it means that it exceeds the risk threshold in normal review).

[0029] like Figure 3 As shown, if the intelligent analysis module is used to adjust the constant The value of , then we have:

[0030] From the above calculation, we can know that the comprehensive deviation of engineering material price and quantity can be determined. The greater the combined deviation in the price and quantity of construction materials, the greater the possibility that there is false or missing information in the construction cost.

[0031] If, the change signature density index is taken (Change signature density index = cumulative change amount / initial contract amount × 100%. Among them, when the change signature density index is greater than 15%, it means that the value exceeds the risk threshold in the normal engineering review procedure).

[0032] The progress payment deviation rate of the contract is (The progress payment deviation rate of the contract = (proportion paid - actual progress completed) / actual progress completed × 100%. When the progress payment deviation rate of the contract is greater than 15%, it means that the value exceeds the risk threshold in the normal engineering review procedure.) Then:

[0033] From the above calculation, we can know that the condition coefficient for the existence of false or missing information in the project cost is , then it means that the results of the analysis of the engineering cost data collected in this consultation show that there is false or missing information.

[0034] Example 4 like Figure 1 As shown, the intelligent analysis module also uses multimodal analysis to identify possible false or missing information in the project cost, and then uses dynamic models to optimize adjustable constants. The intelligent analysis module directly accesses structured and unstructured data from different devices or systems, analyzes and provides conditional coefficients that can be used to determine whether there is false or missing information in the project cost. If we compare the parameters collected when calculating multiple sets of conditional coefficients for false or missing information in the construction cost, we have: Table 1 Relationship between some implementation parameters and false or missing information in project cost From the data in Table 1 above, we can know that when the sample implementation data tends to infinity, there will be a dividing line that uses the conditional coefficient G to divide the data into abnormal and abnormal ones, that is, when When , it means that the analysis results of the engineering cost data collected in this consultation show that there is false or missing information; when , it means that the analysis results of the engineering cost data collected in this consultation show that there is no false or missing information.

[0035] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An engineering consulting management system based on big data, characterized in that: include: The data collection module collects data on project costs, including material declared prices, market average prices, project review quantities, project review quantities, cumulative change amounts, initial contract amounts, proportion of project payments paid, and actual project completion progress, by accessing multi-source heterogeneous data. The data processing module is used to process and analyze the data collected by the data acquisition module, and then calculate the deviation of material unit price, deviation of engineering quantity, density of change signatures and deviation of progress payment; The intelligent analysis module is used to receive the calculation data fed back by the data processing module, and use the integrated calculation engine to determine whether there is any false or missing information in the project cost.

2. The engineering consulting management system based on big data according to claim 1 is characterized in that: The engineering consulting management system based on big data also includes a data storage module; The data storage module is used to construct a storage system that combines multi-source heterogeneous data and non-multi-source heterogeneous data.

3. The engineering consulting management system based on big data according to claim 1 is characterized in that: The data acquisition module also performs preliminary data processing near the data generation source through the edge computing node.

4. The engineering consulting management system based on big data according to claim 1 is characterized in that: The data information fed back by the integrated calculation engine data processing module in the intelligent analysis module is used to calculate the condition coefficient for false or missing information in the project cost, then: ； Where, A conditional coefficient indicating the presence of false or missing information in the project cost; Indicates the comprehensive deviation of engineering material price and quantity; represents the change signature density index; Indicates the progress payment deviation rate of the contract.

5. The engineering consulting management system based on big data according to claim 4 is characterized in that: The intelligent analysis module determines the comprehensive deviation of engineering material price and quantity based on the deviation of material unit price and engineering quantity. , then we have: ； Where, Represents an adjustable constant used to control the rate of change of the integrated deviation and determine the sensitivity of the model; Indicates the impact of the deviation of the material unit price on the comprehensive deviation; Indicates the deviation rate of material unit price; Indicates the influence of the deviation of engineering quantity on the comprehensive deviation; Indicates the deviation rate of engineering quantity.

6. The engineering consulting management system based on big data according to claim 4 is characterized in that: when When , it means that the analysis results of the engineering cost data collected in this consultation show that there is false or missing information; when , it means that the analysis results of the engineering cost data collected in this consultation show that there is no false or missing information.

7. The engineering consulting management system based on big data according to claim 1 or 5, characterized in that: The intelligent analysis module also uses multimodal analysis to identify possible false or missing information in the project cost, and then uses dynamic models to optimize adjustable constants. value.

8. The engineering consulting management system based on big data according to claim 1 or 6, characterized in that: The intelligent analysis module directly accesses structured and unstructured data from different devices or systems to analyze and provide conditional coefficients that can determine whether there is false or missing information in the project cost. threshold.

Images