Big data engineering cost real-time analysis and management method and system and medium

Abstract

The invention relates to a big data project cost real-time analysis and management method and system and a medium, and relates to the technical field of project cost, and the method comprises the steps: extracting a keyword for a user to carry out project cost consultation, and obtaining project progress information in the keyword; judging the progress difference between the actual construction progress of the current project and the project progress information; if the actual construction progress is earlier than the project progress information, capturing market material information through a big data technology, and predicting the project cost corresponding to the project progress information; and if the actual construction progress is later than the project progress information, calculating the project cost corresponding to the project progress information according to the input material cost information. According to market changes and the actual construction progress of the project, the project cost can be analyzed in real time according to the content of project cost consultation, and then the accuracy of project cost analysis is improved.

Description

Technical Field

[0001] The present application relates to the field of engineering cost technology, and in particular to a method, system and medium for real-time analysis and management of big data engineering cost. Background Art

[0002] Project cost consulting is a professional service throughout the entire life cycle of a construction project, involving various links from preliminary feasibility analysis, cost estimation, economic benefit evaluation, engineering design budget, construction budget preparation, engineering settlement, project completion settlement, bidding price formulation and review, etc. In addition, by monitoring the project cost, providing cost information consulting, ensuring that the project cost is controlled within a reasonable range and maximizing economic benefits.

[0003] In all aspects of engineering cost consulting, dynamic monitoring of engineering costs plays a vital role, directly affecting the accuracy of engineering project decisions and ensuring the efficient use and reasonable allocation of investment funds. Through refined cost monitoring, costs can be effectively controlled and unnecessary waste can be avoided, thereby maximizing the investment benefits of construction projects and improving the overall economy and competitiveness of the project.

[0004] However, for a long time, the focus of project cost control has often been on the review of project prices during the bidding and final settlement stages, which has led to a blind spot in the dynamic monitoring of costs during the critical stage from bidding to final settlement. During this period, the volatility of building material market prices is difficult to monitor and reflect in a timely manner, and supervisors are often unable to quickly capture market changes, making it impossible to make timely and effective adjustments to project costs. This lag not only increases the difficulty of project cost control, but may also affect the economic benefits and smooth progress of the project. Summary of the invention

[0005] The first purpose of this application is to provide a big data engineering cost real-time analysis management method, which can conduct real-time analysis of engineering cost based on the content of engineering cost consulting according to market changes and the actual construction progress of the project, thereby improving the accuracy of engineering cost analysis.

[0006] In the first aspect, the present application provides a big data engineering cost real-time analysis and management method, which adopts the following technical solutions: A big data engineering cost real-time analysis and management method, comprising: Extracting keywords used by users for engineering cost consultation and obtaining engineering progress information from the keywords; Determine the progress difference between the actual construction progress of the current project and the progress information of the project; If the actual construction progress is earlier than the project progress information, the market material information is captured by big data technology to predict the project cost corresponding to the project progress information; If the actual construction progress is later than the project progress information, the project cost corresponding to the project progress information is calculated based on the input material cost information.

[0007] By adopting the above technical solution, when the actual construction progress is earlier than the actual construction progress, the market material information is captured through big data technology to predict the future project cost and facilitate procurement; when the actual construction progress is later than the actual construction progress, the current project cost is accurately calculated based on the material cost data provided by the supplier to ensure the real-time and accuracy of cost control, and then carry out real-time analysis of the project cost under different construction progresses in a targeted manner.

[0008] In a preferred example, the present application may be further configured as follows: the step of obtaining the engineering progress information in the keyword and determining the progress difference between the engineering progress information and the actual construction progress of the current project includes: According to the construction activities in the project progress information, the start time and the end time of the construction activities in the progress schedule pre-compiled in the BIM model are compared to generate a comparison time range; Obtain the construction status of the current project through the BIM model, match the construction status to the pre-compiled schedule, and obtain the actual construction progress; Extracting the time nodes of the actual construction progress; Determine whether the time node of the actual construction progress is within the comparison time range.

[0009] By adopting the above technical solution, according to whether the time node corresponding to the current actual construction progress is within the comparison time range of the project progress information, the progress difference between the project progress information of the engineering cost consulting and the actual construction progress can be identified, and then the choice is made whether to predict and estimate the project cost or to choose to accurately calculate the project cost, thereby improving the flexibility and pertinence of the engineering cost consulting.

[0010] In a preferred example, the present application may be further configured as follows: the step of determining the progress difference between the actual construction progress of the current project and the project progress information further includes: If the time node corresponding to the actual construction progress is within the comparison time range of the project progress information, then based on the time node of the actual construction progress, the comparison time range is divided into a construction period and a non-construction period; For the completed construction period, query the supplier material cost information used in the completed construction period through the procurement demand management table in the SRM, and calculate the corresponding project cost of the completed construction period; For the non-construction time period, market material information is captured by big data technology to predict the corresponding project cost for the non-construction time period; The construction cost of the construction period and the construction period not under construction are added together to obtain the construction cost corresponding to the construction progress information.

[0011] By adopting the above technical solution, since the construction period of engineering projects is often long, the market material prices may fluctuate due to various factors during this period. During the non-construction period, big data technology can be used to capture market material price information in real time or regularly to help predict future price trends, thereby more accurately evaluating the project cost of the unconstructed part, and then combining the project cost of the construction period to realize the calculation of the project cost in the project cost consultation.

[0012] In a preferred example, the present application can be further configured as follows: if the actual construction progress is earlier than the project progress information, the step of capturing market material information through big data technology and predicting the project cost corresponding to the project progress information includes: If the time node corresponding to the actual construction progress is before the comparison time range of the project progress information, query the predicted progress information corresponding to the comparison time range in the progress plan table; Based on the material requirements in the forecast progress information, crawler technology is used to capture several procurement information of the material requirements in several target procurement platforms; Extracting procurement keywords related to material cost, freight cost and favorable comment rate from the procurement information, and updating the procurement demand management table in the SRM based on the procurement keywords; Combined with the labor cost and equipment cost of the predicted progress information, the total procurement cost in the updated procurement demand management table is calculated to predict the project cost corresponding to the project progress information.

[0013] By adopting the above technical solution, when the actual construction progress is earlier than the project progress information, the crawler technology can be used to obtain the procurement information of the corresponding materials of several target procurement platforms in advance, and the project cost can be predicted based on the procurement information, so as to respond to the fluctuation of market material prices in time, which is conducive to the effective control and optimization of construction costs.

[0014] In a preferred example, the present application may be further configured as follows: if the actual construction progress is later than the project progress information, the step of calculating the project cost corresponding to the project progress information according to the input material cost information includes: If the time node corresponding to the actual construction progress is after the comparison time range of the project progress information, the material cost information of all suppliers within the comparison time range corresponding to the project progress information is queried through the procurement demand management table in SRM, and the project cost corresponding to the project progress information is calculated.

[0015] By adopting the above technical solution, when the actual construction progress is later than the project progress information, the supplier material cost information of the time node corresponding to the actual construction progress can be queried through the procurement demand management table in SRM, so as to achieve more accurate project cost calculation.

[0016] In a preferred example, the present application may be further configured as follows: after the step of extracting a plurality of procurement keywords related to material cost, freight cost and favorable review rate from the procurement information and updating the procurement demand management table in the SRM based on the plurality of procurement keywords, the step further includes: Calculate and obtain the estimated purchase cost based on the material cost and freight cost of each purchase information in the purchase demand management table; A cost-effectiveness score is calculated based on the ratio of the favorable review rate of each type of procurement information to the estimated procurement cost, and the supplier with the highest cost-effectiveness score is selected for procurement.

[0017] By adopting the above technical solution, the cost-effectiveness score is calculated by considering the ratio of the favorable comment rate of each procurement information to the estimated procurement cost, and the supplier with the highest cost-effectiveness score is selected for procurement. This can effectively reduce procurement costs while ensuring quality and improve the efficiency and economy of overall project cost management.

[0018] In a preferred example, the present application may be further configured as follows: the step of calculating a cost-effectiveness score based on the ratio of the favorable comment rate of each type of the procurement information to the estimated procurement cost, and selecting the supplier with the highest cost-effectiveness score for procurement includes: If the cost-performance ratio scores are the same, obtaining the procurement information of each historical procurement node recorded in the procurement demand management table; According to the procurement information of each procurement node, the cost performance score of each historical procurement node is calculated and obtained, and a cost performance change curve is generated; Select the supplier whose price / performance ratio curve has the smallest fluctuation for purchasing.

[0019] By adopting the above technical solution, when the cost-effectiveness scores of procurement information provided by multiple suppliers are the same, the supplier selection strategy can be further optimized. Not only the cost-effectiveness of the current procurement cycle is considered, but also the historical data is comprehensively considered to ensure that suppliers with long-term stable performance are selected, thereby reducing procurement risks and improving procurement efficiency and reliability.

[0020] In the second aspect, the present application provides a big data engineering cost real-time analysis and management system, which adopts the following technical solutions: A big data engineering cost real-time analysis and management system, comprising: In a third aspect, the present application provides an electronic device, which adopts the following technical solution: Cost consulting module: used to extract keywords for users to consult about project cost and obtain project progress information from the keywords; Progress judgment module: used to judge the progress difference between the actual construction progress of the current project and the progress information of the project; Cost prediction module: used to capture market material information through big data technology and predict the project cost corresponding to the project progress information if the actual construction progress is earlier than the project progress information; Cost calculation module: used to calculate the project cost corresponding to the project progress information based on the input material cost information if the actual construction progress is later than the project progress information.

[0021] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned big data engineering cost real-time analysis and management method are implemented.

[0022] In a fourth aspect, the present application provides a computer storage medium, the following technical solution: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned big data engineering cost real-time analysis and management method.

[0023] In summary, this application has the following beneficial technical effects: This application can conduct real-time analysis of the project cost based on the content of the project cost consulting. When the actual construction progress appearing in the content of the project cost consulting is earlier than the actual construction progress, the market material price trend can be analyzed through big data to predict changes in market material information and thus predict future project costs. When the actual construction progress lags behind the plan, the current project cost can be accurately assessed based on the entered material cost information. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a big data engineering cost real-time analysis and management method in one of the embodiments of the present application.

[0025] Figure 2 It is a sub-step flow chart of step S2 in one embodiment of the present application.

[0026] Figure 3 It is a flowchart of the sub-steps of step S2 in one embodiment of the present application.

[0027] Figure 4 It is a sub-step flow chart of step S3 in one embodiment of the present application.

[0028] Figure 5 It is a sub-step flow chart of step S4 in one embodiment of the present application.

[0029] Figure 6 This is a flowchart of steps added after step S32 in one embodiment of the present application.

[0030] Figure 7 It is a sub-step flow chart of step S321 in one embodiment of the present application.

[0031] Figure 8 It is a structural diagram of a big data engineering cost real-time analysis and management system according to one embodiment of the present application.

[0032] Fig. 9 It is a principle block diagram of an electronic device in one embodiment of the present application.

[0033] Figure numerals: 1. Cost consulting module; 2. Progress judgment module; 3. Cost prediction module; 4. Cost calculation module. DETAILED DESCRIPTION

[0034] The following is combined with Figure 1-9 This application is described in further detail.

[0035] refer to Figure 1 , a big data engineering cost real-time analysis and management method, specifically comprising: S1. Extract keywords used by users for engineering cost consultation and obtain engineering progress information from the keywords.

[0036] Among them, when users conduct engineering cost consultation, they can use smart terminal devices to directly select engineering cost consultation for a certain project progress, or they can directly enter voice commands for engineering cost consultation.

[0037] For the voice commands input by the user, the voice commands are first converted into text data. During the voice recognition process, the voice and related features are extracted from the voice signal to help identify and understand the keywords in the voice commands. Then, NLP technology is used to extract keywords related to the project progress information in the voice commands, which includes construction behaviors and / or time nodes.

[0038] S2. Determine the progress difference between the actual construction progress of the current project and the project progress information.

[0039] Among them, the progress difference is the difference in time between the progress corresponding to the project progress information consulted by the user and the actual construction progress of the current project. By judging the progress difference, it can be determined whether the progress of the project cost consulted by the user is ahead of the actual construction progress. If it is ahead, it means that the project consulted by the user has been completed, and the material cost and labor cost used have been determined; if it is after, it means that the project consulted by the user has not started yet, and the material cost and labor cost used have not been determined.

[0040] S3. If the actual construction progress is earlier than the project progress information, the market material information is captured through big data technology to predict the project cost corresponding to the project progress information.

[0041] Among them, if the actual construction progress is earlier than the project progress information, the project cost consultation is to predict the subsequent project cost. Since each construction step lasts for a long time, that is, the interval from one construction step to the next construction step is long, and the materials have not been put into use at this time, the market material information captured by big data technology is used to predict the project cost based on market changes and the actual construction progress of the project.

[0042] At the same time, if the materials in the project progress information have been confirmed and will not change, the material cost information already entered in the procurement demand management table is used to accurately calculate the project cost corresponding to the project progress information.

[0043] S4. If the actual construction progress is later than the project progress information, the project cost corresponding to the project progress information is calculated based on the input material cost information.

[0044] Among them, if the actual construction progress is later than the project progress information, the project cost consultation is to query the cost of the completed project. Therefore, the entered material cost information is directly used to accurately calculate the project cost corresponding to the project progress information.

[0045] In this embodiment, the progress difference between the actual construction progress and the project progress information determines whether to calculate the historical project cost or predict the future project cost, so as to achieve targeted calculation of the project cost according to the actual construction progress and improve the accuracy of the project cost analysis.

[0046] refer to Figure 2 Further, in one embodiment, step S2 is refined into the following sub-steps: S20. According to the construction activities in the project progress information, the start time and the end time of the construction activities in the pre-compiled progress schedule in the BIM model are compared to generate a comparison time range.

[0047] Among them, construction behaviors include but are not limited to the construction time nodes, construction locations, construction materials and construction equipment of the construction project, and there are corresponding data records in the pre-compiled schedule in the BIM model. By comparing the construction behaviors in the project progress information with the construction behaviors in the schedule, the start time and end time of the construction behaviors in the project progress information can be obtained. In addition, since construction behaviors are divided into those that have occurred and those that have not occurred, the start time and end time provided by the schedule may not have started, are in progress, or have ended. The time period between the start time and the end time is set as the comparison time range, which is used for subsequent comparison with the time nodes of the actual construction progress.

[0048] S21. Obtain the construction status of the current project through the BIM model, match the construction status to the pre-compiled schedule, and obtain the actual construction progress.

[0049] Among them, the BIM model can record the construction status of the current project in real time. By aligning and fusing the BIM model with the three-dimensional point cloud data collected at the construction site, the construction progress monitoring can be realized to obtain the actual construction progress of the current project. Specifically, it is necessary to convert the original BIM model data into a data structure that can read the vertex coordinate information, select the same-name points in the BIM model and the three-dimensional point cloud, and use the least squares calculation method to obtain the rotation and translation transformation relationship between the two models to achieve high-precision alignment. Then match the current construction situation with the schedule to obtain the actual construction progress recorded in the schedule. From the actual construction progress, data such as the construction time node, construction location, construction materials, construction equipment, and construction labor costs can be obtained.

[0050] S22, extracting the time nodes of the actual construction progress.

[0051] S23. Determine whether the time node of the actual construction progress is within the comparison time range.

[0052] In this embodiment, the deviation between the project progress in the engineering cost consulting and the actual construction progress is identified based on whether the time node corresponding to the actual construction progress is within the comparison time range of the project progress information, which helps to determine whether to adopt a predictive estimate of the project cost or to perform a more accurate cost calculation, thereby improving the adaptability and accuracy of the engineering cost consulting, and ensuring that the engineering cost consulting can not only flexibly respond to different situations, but also provide accurate information based on specific needs.

[0053] In addition, reference Figure 3 Furthermore, in one embodiment, step S2 is further divided into the following sub-steps: S24. If the time node corresponding to the actual construction progress is within the comparison time range of the project progress information, the comparison time range is divided into a construction time period and a non-construction time period based on the time node of the actual construction progress.

[0054] Among them, the construction period can accurately calculate the project cost, while the non-construction period requires the prediction of the project cost. By distinguishing the two different project cost calculation methods, it is possible to analyze and query the project cost consulting in which the time node corresponding to the actual construction progress is between the comparison time range of the project progress information, thereby improving the versatility of the project cost consulting.

[0055] S25. For the construction period, query the supplier material cost information used in the construction period through the procurement demand management table in the SRM, and calculate the corresponding project cost of the construction period.

[0056] Among them, the procurement demand management table in SRM records the material cost information of the purchased suppliers, and the material cost information is used to accurately calculate the project cost corresponding to the construction period.

[0057] S26. For the non-construction time period, the market material information is captured through big data technology to predict the corresponding project cost for the non-construction time period.

[0058] Among them, some market material information includes material cost, freight cost, praise rate and other data. The market material information captured by big data technology is used to predict the project cost corresponding to the non-construction period.

[0059] S27. Add the construction costs of the construction period and the non-construction period to obtain the construction cost corresponding to the construction progress information.

[0060] In this embodiment, engineering projects usually have a long construction period, during which the market material prices may fluctuate due to various internal and external factors, which in turn affects the prediction of the project cost. Therefore, big data technology is used to collect market material price information in real time or regularly during the non-construction period to predict future price trends, and to accurately predict the project cost of the non-construction part, and then combine it with the actual project cost calculated during the construction period to calculate the project cost during the entire comparison time range.

[0061] In addition, reference Figure 4 Further, in one embodiment, step S3 is refined into the following sub-steps: S30. If the time node corresponding to the actual construction progress is before the comparison time range of the project progress information, query the forecast demand information corresponding to the comparison time range in the progress plan table.

[0062] S31. Based on the material demand in the predicted demand information, use crawler technology to capture several procurement information of material demand in several target procurement platforms.

[0063] Among them, material demand includes the type of materials required for construction, material cost, labor cost, etc. corresponding to the predicted demand information in the schedule. The same type of materials are captured on several target procurement platforms on the Internet through crawler technology to obtain several procurement information.

[0064] S32. Extract procurement keywords related to material cost, freight cost and favorable comment rate from a number of procurement information, and update the procurement demand management table in SRM based on the several procurement keywords.

[0065] Among them, the purchasing keywords include but are not limited to material cost, freight cost and favorable comment rate. In other embodiments, preferential policies, discount standards, etc. are also included, and users can add or delete them according to actual conditions.

[0066] S33. Combine the labor cost and equipment cost of the forecast demand information to calculate the total procurement cost in the updated procurement demand management table to forecast the project cost corresponding to the project progress information.

[0067] Among them, according to the schedule, considering the complexity of the project, construction technical requirements and construction schedule, the number of workers required for each construction node is determined, and then according to the list of construction personnel, the salary composition of all types of work is obtained, and then the labor cost is obtained through product operation. At the same time, according to the schedule, the type and quantity of equipment invested in engineering cost consulting are obtained, and the equipment cost is obtained through product operation.

[0068] In this embodiment, when the actual construction progress is ahead of the project progress information, crawler technology is used to pre-capture the procurement information of the required materials from several target procurement platforms. Based on the procurement information, dynamic prediction of the project cost is performed to respond to fluctuations in market material prices in a timely manner, thereby improving the accuracy of predicted project costs and contributing to the effective control and optimization of construction costs.

[0069] In addition, reference Figure 5 Further, in one embodiment, step S4 is refined into the following sub-steps: S40. If the time node corresponding to the actual construction progress is after the comparison time range of the project progress information, the material cost information of all suppliers within the comparison time range corresponding to the project progress information is queried through the procurement demand management table in SRM, and the project cost corresponding to the project progress information is calculated.

[0070] Among them, when the time node corresponding to the actual construction progress is after the comparison time range of the project progress information, the project progress information queried by the project cost consulting is that the construction has been completed. The project cost calculation is realized by directly querying the material cost information of the procurement demand management table in the SRM.

[0071] In this embodiment, when the actual construction progress is later than the project progress information, the supplier material cost information at the time node corresponding to the actual construction progress is queried through the procurement demand management table in the SRM. Based on the actual procurement quantity, the project cost within the comparison time range is accurately calculated.

[0072] In addition, reference Figure 6 Further, in one embodiment, steps S320 and S321 are added after step S32: S320. Calculate and obtain the estimated purchase cost based on the material cost and freight cost of each purchase information in the purchase demand management table.

[0073] S321. Calculate the cost-effectiveness score based on the ratio of the favorable comment rate of each type of procurement information to the estimated procurement cost, and select the supplier with the highest cost-effectiveness score for procurement.

[0074] Among them, the cost-effectiveness score = favorable comment rate / procurement cost. By scoring the cost-effectiveness, the supplier with the highest cost-effectiveness score is selected for procurement, which can not only improve the quality of the purchased materials, but also effectively control the procurement cost, thereby improving the efficiency and economic benefits of the entire project cost management.

[0075] In addition, reference Figure 7 Further, in one embodiment, step S321 is refined into the following sub-steps: S3210. If the cost-performance ratio scores are the same, the procurement information of each historical procurement node recorded in the procurement demand management table is obtained.

[0076] Among them, when the current cost-effectiveness scores of the initially screened suppliers are the same, the procurement information of each supplier at the historical procurement node recorded in the procurement demand management table is further retrieved to more comprehensively evaluate the supplier's long-term performance.

[0077] S3211. Based on the procurement information of each procurement node, calculate and obtain the cost-effectiveness score of each historical procurement node, and generate a cost-effectiveness change curve.

[0078] Among them, based on historical procurement information, the cost-performance score of each supplier at different procurement nodes is recalculated, and then these data points are connected to form a cost-performance change curve. The cost-performance change curve can intuitively show the fluctuation of the cost-performance of materials provided by suppliers, making it easier to identify the stability of suppliers, avoid selecting suppliers with large fluctuations in material cost-performance, and reduce uncertainty in future procurement.

[0079] S3212. Choose the supplier with the smallest fluctuation in price / performance ratio curve for purchasing.

[0080] In this embodiment, when the cost-effectiveness scores of purchasing the same material from multiple suppliers are the same, the long-term performance stability of the suppliers is comprehensively evaluated by analyzing historical procurement information, and the continuous cost-effectiveness advantages and stability demonstrated by the selected supplier in each historical procurement node are evaluated, thereby effectively reducing procurement risks and improving the efficiency and reliability of procurement decisions.

[0081] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0082] The embodiment of the present application also provides a big data engineering cost real-time analysis and management system, which corresponds one-to-one to the big data engineering cost real-time analysis and management method in the embodiment.

[0083] refer to Figure 8 A big data engineering cost real-time analysis and management system includes: cost consultation module 1, progress judgment module 2, cost prediction module 3, and cost calculation module 4. The detailed description of each functional module is as follows: Cost consulting module 1: used to extract keywords used by users for engineering cost consulting and obtain engineering progress information from the keywords.

[0084] Progress judgment module 2: used to judge the progress difference between the actual construction progress of the current project and the project progress information.

[0085] Cost prediction module 3: If the actual construction progress is earlier than the project progress information, the market material information is captured through big data technology to predict the project cost corresponding to the project progress information.

[0086] Cost calculation module 4: used to calculate the project cost corresponding to the project progress information based on the input material cost information if the actual construction progress is later than the project progress information.

[0087] Among them, the cost consulting module 1 extracts the keywords in the engineering cost consulting and the engineering progress information in the keywords, and then the progress judgment module 2 can make a progress difference judgment based on the engineering progress information provided by the keywords to confirm whether the engineering progress mentioned in the engineering cost consulting has occurred. If it has not occurred, that is, the actual construction progress is earlier than the engineering progress information, then the cost prediction module 3 will use the market material information captured by big data to predict the engineering cost of the engineering cost consulting and predict the future engineering cost; if it has occurred, that is, the actual construction progress is later than the engineering progress information, then the cost calculation module 4 will perform accurate engineering cost calculation based on the input material cost information. Through the combination of various modules, it is possible to analyze the engineering cost in real time according to the market changes and the actual construction progress of the project, and improve the accuracy of the engineering cost analysis.

[0088] For the specific definition of the big data engineering cost real-time analysis management system, please refer to the definition of the big data engineering cost real-time analysis management method in the context, which will not be repeated here. Each module in the above-mentioned big data engineering cost real-time analysis management system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. In one embodiment, an electronic device is provided, which is a user terminal. Reference Fig. 9 , the electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a detection data table. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a real-time analysis and management method for big data engineering cost is implemented.

[0089] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1. Extract keywords used by users for engineering cost consultation and obtain engineering progress information from the keywords.

[0090] S2. Determine the progress difference between the actual construction progress of the current project and the project progress information.

[0091] S3. If the actual construction progress is earlier than the project progress information, the market material information is captured through big data technology to predict the project cost corresponding to the project progress information.

[0092] S4. If the actual construction progress is later than the project progress information, the project cost corresponding to the project progress information is calculated based on the input material cost information.

[0093] In one embodiment, the sub-steps of step S2 include: S20. According to the construction activities in the project progress information, the start time and the end time of the construction activities in the pre-compiled progress schedule in the BIM model are compared to generate a comparison time range.

[0094] S21. Obtain the construction status of the current project through the BIM model, match the construction status to the pre-compiled schedule, and obtain the actual construction progress.

[0095] S22, extracting the time nodes of the actual construction progress.

[0096] S23. Determine whether the time node of the actual construction progress is within the comparison time range.

[0097] In one embodiment, step S2 is further divided into sub-steps including: S24. If the time node corresponding to the actual construction progress is within the comparison time range of the project progress information, the comparison time range is divided into a construction time period and a non-construction time period based on the time node of the actual construction progress.

[0098] S25. For the construction period, query the supplier material cost information used in the construction period through the procurement demand management table in the SRM, and calculate the corresponding project cost of the construction period.

[0099] S26. For the non-construction time period, the market material information is captured through big data technology to predict the corresponding project cost for the non-construction time period.

[0100] S27. Add the construction costs of the construction period and the non-construction period to obtain the construction cost corresponding to the construction progress information.

[0101] In one embodiment, the sub-steps of step S3 include: S30. If the time node corresponding to the actual construction progress is before the comparison time range of the project progress information, query the forecast demand information corresponding to the comparison time range in the progress plan table.

[0102] S31. Based on the material demand in the predicted demand information, use crawler technology to capture several procurement information of material demand in several target procurement platforms.

[0103] S32. Extract procurement keywords related to material cost, freight cost and favorable comment rate from a number of procurement information, and update the procurement demand management table in SRM based on the several procurement keywords.

[0104] S33. Combine the labor cost and equipment cost of the forecast demand information to calculate the total procurement cost in the updated procurement demand management table to forecast the project cost corresponding to the project progress information.

[0105] In one embodiment, the sub-steps of step S4 include: S40. If the time node corresponding to the actual construction progress is after the comparison time range of the project progress information, the material cost information of all suppliers within the comparison time range corresponding to the project progress information is queried through the procurement demand management table in SRM, and the project cost corresponding to the project progress information is calculated.

[0106] In one embodiment, the steps added after step S32 include: S320. Calculate and obtain the estimated purchase cost based on the material cost and freight cost of each purchase information in the purchase demand management table.

[0107] S321. Calculate the cost-effectiveness score based on the ratio of the favorable comment rate of each type of procurement information to the estimated procurement cost, and select the supplier with the highest cost-effectiveness score for procurement.

[0108] In one embodiment, the sub-steps of step S321 include: S3210. If the cost-performance ratio scores are the same, the procurement information of each historical procurement node recorded in the procurement demand management table is obtained.

[0109] S3211. Based on the procurement information of each procurement node, calculate and obtain the cost-effectiveness score of each historical procurement node, and generate a cost-effectiveness change curve.

[0110] S3212. Choose the supplier with the smallest fluctuation in price / performance ratio curve for purchasing.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A big data engineering cost real-time analysis and management method, characterized in that: include: Extracting keywords used by users for engineering cost consultation and obtaining engineering progress information from the keywords; Determine the progress difference between the actual construction progress of the current project and the progress information of the project; If the actual construction progress is earlier than the project progress information, the market material information is captured by big data technology to predict the project cost corresponding to the project progress information; If the actual construction progress is later than the project progress information, the project cost corresponding to the project progress information is calculated based on the input material cost information.

2. The method according to claim 1, characterized in that The step of obtaining the engineering progress information in the keyword and determining the progress difference between the engineering progress information and the actual construction progress of the current project includes: According to the construction activities in the project progress information, the start time and the end time of the construction activities in the progress schedule pre-compiled in the BIM model are compared to generate a comparison time range; Obtain the construction status of the current project through the BIM model, match the construction status to the pre-compiled schedule, and obtain the actual construction progress; Extracting the time nodes of the actual construction progress; Determine whether the time node of the actual construction progress is within the comparison time range.

3. The method according to claim 2, characterized in that The step of determining the progress difference between the actual construction progress of the current project and the project progress information also includes: If the time node corresponding to the actual construction progress is within the comparison time range of the project progress information, then based on the time node of the actual construction progress, the comparison time range is divided into a construction period and a non-construction period; For the completed construction period, query the supplier material cost information used in the completed construction period through the procurement demand management table in the SRM, and calculate the corresponding project cost of the completed construction period; For the non-construction time period, market material information is captured by big data technology to predict the corresponding project cost for the non-construction time period; The construction cost of the construction period and the construction period not under construction are added together to obtain the construction cost corresponding to the construction progress information.

4. The method according to claim 2, characterized in that: If the actual construction progress is earlier than the project progress information, the step of capturing market material information through big data technology and predicting the project cost corresponding to the project progress information includes: If the time node corresponding to the actual construction progress is before the comparison time range of the project progress information, query the predicted progress information corresponding to the comparison time range in the progress plan table; Based on the material requirements in the forecast progress information, crawler technology is used to capture several procurement information of the material requirements in several target procurement platforms; Extracting procurement keywords related to material cost, freight cost and favorable comment rate from the procurement information, and updating the procurement demand management table in the SRM based on the procurement keywords; Combined with the labor cost and equipment cost of the predicted progress information, the total procurement cost in the updated procurement demand management table is calculated to predict the project cost corresponding to the project progress information.

5. The method according to claim 2, characterized in that: If the actual construction progress is later than the project progress information, the step of calculating the project cost corresponding to the project progress information according to the input material cost information includes: If the time node corresponding to the actual construction progress is after the comparison time range of the project progress information, the material cost information of all suppliers within the comparison time range corresponding to the project progress information is queried through the procurement demand management table in SRM, and the project cost corresponding to the project progress information is calculated.

6. The method according to claim 4, characterized in that After the step of extracting a plurality of procurement keywords related to material cost, freight cost and favorable comment rate from the procurement information and updating the procurement demand management table in the SRM based on the plurality of procurement keywords, the step further includes: Calculate and obtain the estimated purchase cost based on the material cost and freight cost of each purchase information in the purchase demand management table; A cost-effectiveness score is calculated based on the ratio of the favorable review rate of each type of procurement information to the estimated procurement cost, and the supplier with the highest cost-effectiveness score is selected for procurement.

7. The method according to claim 6, characterized in that The step of calculating a cost-effectiveness score based on the ratio of the favorable review rate of each type of procurement information to the estimated procurement cost, and selecting the supplier with the highest cost-effectiveness score for procurement includes: If the cost-performance ratio scores are the same, obtaining the procurement information of each historical procurement node recorded in the procurement demand management table; According to the procurement information of each procurement node, the cost performance score of each historical procurement node is calculated and obtained, and a cost performance change curve is generated; Select the supplier whose price / performance ratio curve has the smallest fluctuation for purchasing.

8. A big data engineering cost real-time analysis and management system, characterized in that: include: Cost consulting module (1): used to extract keywords used by users for engineering cost consulting and obtain engineering progress information from the keywords; Progress judgment module (2): used to judge the progress difference between the actual construction progress of the current project and the progress information of the project; Cost prediction module (3): used to capture market material information through big data technology and predict the construction cost corresponding to the construction progress information if the actual construction progress is earlier than the construction progress information; Cost calculation module (4): used to calculate the construction cost corresponding to the construction progress information based on the input material cost information if the actual construction progress is later than the construction progress information.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes any one of the big data engineering cost real-time analysis and management methods as claimed in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute a method for real-time analysis and management of construction cost of big data as claimed in any one of claims 1 to 7.

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