Method and system for measuring and calculating construction cost

Abstract

The invention relates to the technical field of project cost, in particular to a project cost measuring and calculating method and system. The method comprises the following steps: obtaining each risk sequence constructed by historical risk information in a historical construction process, and obtaining a plurality of initial risk points according to similar conditions among the risk sequences; constructing a risk vector based on the occurrence frequency of the keywords in the initial risk point; clustering the initial risk points according to the similarity between the risk vectors to obtain a target cluster, potential risk points and risk types; according to the triggering reasons of the risk types of the potential risk points and the total number of the risk types, the occurrence probability of the potential risk points is obtained; and determining the prediction cost of the current project by combining the affected condition corresponding to the potential risk point in the historical risk information, the overall similar condition between the risk vectors in the target cluster where the potential risk point is located, the occurrence probability and the initial cost of the current project. According to the invention, the measurement and calculation efficiency of the engineering project cost and the accuracy of the measurement and calculation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering cost, and in particular to an engineering cost estimation method and system. Background Art

[0002] Project cost estimation refers to the process of making detailed estimates and calculations of various expenses and costs of a project during the construction of the project. By accurately estimating the project cost, the required funds can be determined before the project is implemented, avoiding the problem of over-budget during the project progress, thereby ensuring that the project proceeds smoothly within the predetermined funding framework. At the same time, it can also be used to determine whether the project is advancing, improve the overall efficiency of the project, and avoid possible cost risks in the project. Among them, the analysis of potential risk points has a great impact on the overall cost estimation results. The more complete the potential risk point analysis is, the more accurate the cost estimation results will be.

[0003] When predicting potential risk points, existing methods usually summarize the potential risk points that may appear through field investigations, expert opinion summary, and artificial intelligence analysis, and then obtain the impact of potential risk points on cost estimation results based on the summary results. However, in the prediction method of potential risk points, the summary results of potential risk points are usually rough, that is, when analyzing the possibility and impact of risks, they are easily affected by factors such as the subjective impression of the assessors and the richness of the data content, resulting in deviations in the assessment results. At the same time, when analyzing potential risk points, a large amount of data and more complex calculations are required, resulting in a large probability of error in the prediction results, which in turn affects the accuracy of cost estimation. Summary of the invention

[0004] In order to solve the problem of low accuracy of the calculation results in the process of calculating the cost of engineering projects in the existing methods, the purpose of the present invention is to provide a method and system for calculating the construction cost. The technical scheme adopted is as follows: In a first aspect, the present invention provides a method for calculating construction cost, the method comprising the following steps: Obtain historical risk information from historical construction processes and construct risk sequences based on the historical risk information; According to the similarity between risk sequences, the risk sequences under the same information source are divided into several groups; risk vectors are constructed based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; the initial risk points are clustered according to the similarity between risk vectors to obtain several target clusters, potential risk points and risk types; According to the triggering reasons of the risk types of potential risk points and the total number of risk types, the occurrence probability of potential risk points is obtained; the overall impact weight of potential risk points on the project is obtained by combining the impact conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, the occurrence probability and the initial cost of the current project; the predicted risk cost of the current project is determined by using the impact conditions, occurrence probability and overall impact weights corresponding to all potential risk points; The predicted cost of the current project is obtained based on the initial cost of the current project and the predicted risk cost.

[0005] Preferably, the risk sequences under the same information source are divided into several groups according to the similarities between the risk sequences, including: For any source: According to the quantity difference and DTW distance of the elements in the risk sequences of any two information sources, the similarity between the risk sequences of any two information sources is obtained, wherein the quantity difference and the DTW distance are negatively correlated with the similarity; A preset number of risk sequences are randomly selected as initial centers, and risk sequences whose similarity with the initial centers is greater than a preset similarity threshold are grouped together.

[0006] Preferably, the step of constructing a risk vector based on the occurrence frequency of keywords in the initial risk point includes: Obtain all keywords in the candidate initial risk points; The vector composed of keywords whose occurrence frequency of all keywords in the candidate initial risk point is greater than the preset frequency threshold is used as the risk vector of the candidate initial risk point; The candidate initial risk point is any initial risk point.

[0007] Preferably, clustering the initial risk points according to the similarities between the risk vectors to obtain a number of target clusters, potential risk points and risk types includes: A hierarchical clustering algorithm is used to cluster all initial risk points, and the average value of the similarities between all risk vectors in each cluster is used as the similarity degree corresponding to each cluster; The layer with the largest discrete degree of similarity corresponding to all clusters is taken as the final clustering result, and each target cluster in the final clustering result is obtained, and each target cluster is taken as a potential risk point; The next layer of the final clustering result is used as the risk type corresponding to each target cluster.

[0008] Preferably, obtaining the occurrence probability of the potential risk point according to the triggering cause of the risk type of the potential risk point and the total number of risk types includes: For any potential risk point: Determining a first occurrence probability of a risk type of any potential risk point according to the total number of risk types; Determining a second occurrence probability of any potential risk point within a corresponding risk type according to the number of triggering causes of any potential risk point; The first occurrence probability and the second occurrence probability are combined to determine the occurrence probability of any potential risk point.

[0009] Preferably, the overall impact weight of the potential risk point on the project is obtained by combining the impact situation corresponding to the potential risk point in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk point is located, the probability of occurrence and the initial cost of the current project, including: For any potential risk point: Calculate the average of the impact amounts corresponding to all historical risks of the potential risk point, and record it as the average impact amount; where the impact amount is the difference between the final cost of the project and the initial estimated cost; calculating a first ratio between the average impact amount and the initial cost of the current project; The normalized result of the product of the similarity degree corresponding to any potential risk point, the average impact amount and the first ratio is determined as the overall impact weight of any potential risk point on the project.

[0010] Preferably, the method of determining the predicted risk cost of the current project by using the impact conditions, occurrence probabilities and overall impact weights corresponding to all potential risk points includes: For any potential risk point: the maximum impact value of any potential risk point is obtained by combining the average impact amount corresponding to any potential risk point, the probability of occurrence of any potential risk point and the overall impact weight of any potential risk point on the project; The cumulative sum of the maximum impact values ​​of all potential risk points is taken as the predicted risk cost of the current project.

[0011] Preferably, obtaining the predicted cost of the current project according to the initial cost and predicted risk cost of the current project includes: The sum of the initial cost of the current project and the predicted risk cost is determined as the predicted cost of the current project.

[0012] Preferably, obtaining the discrete degree of the similarity degrees corresponding to all clusters includes: taking the variance of the similarity degrees corresponding to all clusters as the discrete degree.

[0013] In a second aspect, the present invention provides a system for calculating construction cost, the system comprising: The data collection module is used to obtain historical risk information during the historical construction process and construct various risk sequences based on the historical risk information; The first processing module is used to divide the risk sequences under the same information source into several groups according to the similarities between the risk sequences; construct risk vectors based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; cluster the initial risk points according to the similarities between the risk vectors to obtain several target clusters, potential risk points and risk types; The second processing module is used to obtain the probability of occurrence of potential risk points according to the triggering reasons of the risk types of potential risk points and the total number of risk types; combine the impact conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, the probability of occurrence and the initial cost of the current project to obtain the overall impact weight of the potential risk points on the project; use the impact conditions, probability of occurrence and overall impact weights corresponding to all potential risk points to determine the predicted risk cost of the current project; The prediction module is used to obtain the predicted cost of the current project based on the initial cost and predicted risk cost of the current project.

[0014] The present invention has at least the following beneficial effects: The present invention first divides the risk sequences under the same information source into multiple groups according to the similarities between the risk sequences in the historical risk information during the historical construction process, and each group is regarded as an initial risk point. A risk vector is constructed according to the frequency of occurrence of keywords in the initial risk point, and the initial risk points are clustered into multiple target clusters according to the similarities between the risk vectors, and potential risk points are determined. These risk points are potential risk points that may be encountered in the current project. The potential laws and associations in the data are mined through grouping and clustering, and the historical risk information is summarized, which improves the accuracy of identifying potential risk points. Further, the risk cost of the current project is predicted in combination with the triggering cause of the risk type of the potential risk point, the total number of risk types, the affected conditions corresponding to the potential risk points in the historical risk information, and the initial cost of the current project, and then the predicted cost of the current project is determined in combination with the initial cost of the current project. The present invention constructs a risk database for each potential risk point, so that when performing potential risk analysis, the probability of occurrence of the potential risk point and the impact on the current project can be quickly and accurately output by matching the project type and content, thereby improving the efficiency and accuracy of engineering cost estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 work.

[0016] Figure 1 A flowchart of a method for calculating construction cost provided by an embodiment of the present invention; Figure 2 A structural block diagram of a system for calculating construction cost provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a method and system for calculating the construction cost proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a method and system for calculating construction cost provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] An embodiment of a method for calculating construction cost: The specific scenario targeted by this embodiment is: in the process of calculating the cost of an engineering project, in order to ensure the accuracy of the calculation results, it is necessary to determine the potential risk points of the engineering project, and estimate the impact of the potential risk points, and then obtain the cost amount of the engineering project.

[0021] This embodiment proposes a method for calculating the construction cost. Figure 1 As shown, a method for calculating the construction cost of an engineering project in this embodiment includes the following steps: Step S1, obtaining historical risk information during historical construction processes, and constructing risk sequences based on the historical risk information.

[0022] First, obtain the project documents of all projects in the construction process within the historical time period, including design drawings, contract documents, technical specifications and other information; then, determine the material price of the required raw materials, labor price and the price of various instruments and equipment according to the design drawings and technical specifications; further, understand the relevant policies of the area where the project is located, including tax policies, environmental protection requirements and other information; at the same time, conduct on-site surveys of the construction site to obtain site information, including construction site conditions, project volume and possible problems during the construction process; then, summarize the historical risks of its own historical projects, customer historical projects and other similar projects. It should be noted that the historical risk information summarized above is extracted through artificial intelligence algorithm models, such as BERT models, to extract key information, such as dealer A, the price fluctuation range of each material, etc. The implementers of the historical time period set it according to the specific situation, and no more details will be given here.

[0023] It should be noted that the data and information collected in this application are obtained with full consent and authorization, and the collection, use and processing of relevant information must comply with relevant regulations.

[0024] This embodiment uses the historical risk information collected through the historical construction process as a reference to calculate the cost of the current project.

[0025] Furthermore, for any historical risk, all keywords in the historical risk are extracted, such as: dealer A, price fluctuation range of each material, equipment price fluctuation range, etc., and these keywords are used to construct a risk sequence. Specifically, all keywords in the historical risk are converted into word vectors, and then the word vectors corresponding to the historical risk constitute the risk sequence.

[0026] So far, this embodiment has acquired multiple risk sequences in the historical construction process.

[0027] Step S2, divide the risk sequences under the same information source into several groups according to the similarities between the risk sequences; construct risk vectors based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; cluster the initial risk points according to the similarities between the risk vectors to obtain several target clusters, potential risk points and risk types.

[0028] Since the historical risk information summarized in the preliminary preparations comes from different engineering projects, there are certain differences between different historical risk information, that is, there are differences in the participating parties and project locations, among which the participating parties include contractors, managers and suppliers.

[0029] However, by clustering the historical risk information, classifying it into different potential risk points and constructing a corresponding set of potential risk points, it is possible to associate the historical risks of different engineering projects, thereby improving the accuracy of potential risk point analysis and the difficulty of obtaining it.

[0030] Therefore, the present embodiment will group the risk sequences so that the similarity of all risk sequences in the same group is higher, and the similarity of risk sequences between different groups is lower. The similarity between different risk sequences can be characterized by the dynamic time warping distance. The larger the dynamic time warping distance between two sequences, the lower the matching degree between the two sequences, that is, the less similar the fluctuation degree of the two sequences is. In addition, considering that there may be length differences between two risk sequences, and the dynamic time warping distance cannot intuitively reflect the length differences between different risk sequences, the present embodiment will combine the dynamic time warping distance between different risk sequences and the length differences between different risk sequences to evaluate the similarity between the two sequences, and divide different risk sequences based on the evaluation results.

[0031] In this embodiment, one participant is regarded as one information source. Next, this embodiment is described by taking one information source as an example. The risk sequences of other information sources can be processed by the method provided in this embodiment.

[0032] For any source: The dynamic time warping (DTW) distance between the pairwise risk sequences under the information source is calculated, and the similarity between the pairwise risk sequences under the information source is obtained according to the quantity difference of elements in the pairwise risk sequences under the information source and the DTW distance, wherein the quantity difference and the DTW distance are negatively correlated with the similarity.

[0033] Among them, the negative correlation relationship means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by practical applications.

[0034] In this embodiment, a specific calculation formula for the similarity between risk sequences is given. The similarity between the i-th risk sequence and the j-th risk sequence can be expressed as: in, represents the similarity between the i-th risk sequence and the j-th risk sequence, represents the number of elements in the ith risk sequence, represents the number of elements in the j-th risk sequence, represents the DTW distance between the i-th risk sequence and the j-th risk sequence, Indicates the absolute value sign, Represents the normalization function.

[0035] In this embodiment, the constant 1 is added to the denominator of the similarity calculation formula to prevent the denominator from being 0. In specific applications, the implementer can set it according to specific circumstances. It represents the difference in the number of elements between the ith risk sequence and the jth risk sequence. The larger the value, the greater the length difference between the two sequences. The larger the DTW distance between the ith risk sequence and the jth risk sequence, the lower the matching degree between the two, that is, the lower the similarity. When the difference in the number of elements between the ith risk sequence and the jth risk sequence is smaller, and the DTW distance between the ith risk sequence and the jth risk sequence is smaller, it means that the ith risk sequence is more similar to the jth risk sequence, that is, the greater the similarity between the ith risk sequence and the jth risk sequence.

[0036] By adopting the above method, the similarity between the risk sequences of each pair under the information source can be obtained. A preset number of risk sequences are randomly selected as the initial centers, and the risk sequences whose similarity with each initial center is greater than the preset similarity threshold are taken as a group, that is, the risk sequences under the information source are divided into multiple groups, and each group is taken as an initial risk point, that is, multiple initial risk points are obtained. It should be noted that: if the similarity between a risk sequence and all initial sequences is less than or equal to the preset similarity threshold, it means that the risk sequence may have information collection errors or other situations during data collection, and it will not be analyzed subsequently. In this embodiment, the preset number is 10, and the preset similarity threshold is 0.5. In specific applications, the implementer can set the preset number and preset similarity threshold according to the specific situation.

[0037] Next, this embodiment is described by taking an initial risk point as an example, and other initial risk points can be processed by the method provided in this embodiment.

[0038] Specifically, any initial risk point is recorded as a candidate initial risk point, and all keywords in the candidate initial risk point are obtained; the frequency of occurrence of each keyword in the candidate initial risk point is counted; the vector composed of keywords whose frequency of occurrence of all keywords in the candidate initial risk point is greater than the preset frequency threshold is used as the risk vector of the candidate initial risk point; that is, keywords with a lower frequency of occurrence are eliminated, and the risk vector is composed of keywords with a higher frequency of occurrence. In this embodiment, the preset frequency threshold is 0.4, and in specific applications, the implementer can set it according to specific circumstances.

[0039] Next, this embodiment will use a hierarchical clustering algorithm to cluster all initial risk points under all information sources, so as to obtain clustering results under different division precisions, which is conducive to the aggregation and merging of potential risk points under different information sources. It should be noted that risk types include market risks, technical risks, and management risks, and the triggering reasons include material price fluctuations (market risks), changes in technical standards (technical risks), and delayed supply and substandard quality (management risks).

[0040] Specifically, a hierarchical clustering algorithm is used to perform agglomerative hierarchical clustering on all initial risk points, that is, each sample starts as an independent class, and the classes with the highest similarity are gradually merged until all samples are merged into one class. For each layer of the hierarchical clustering result, the average value of the similarity between all risk vectors in each cluster is calculated and recorded as the similarity corresponding to each cluster. The greater the discreteness of the similarity between different clusters in the same layer, the better the clustering effect; in this embodiment, the variance is used to characterize the discreteness, that is, the variance of the similarity corresponding to all clusters in the same layer is used as the discreteness of the layer. The larger the variance, the greater the discreteness, that is, the better the clustering effect of the layer; the clustering result of the layer with the largest discreteness is used as the final clustering result, and each cluster in the final clustering result is obtained, and these clusters are recorded as target clusters, and each target cluster is used as a potential risk point, that is, multiple potential risk points are obtained, and these potential risk points are potential risk points of the current project. The next layer of the final clustering result is used as the risk type corresponding to each target cluster, that is, multiple risk types are obtained. The hierarchical clustering algorithm is a prior art and will not be described in detail here.

[0041] Step S3, according to the triggering cause of the risk type of the potential risk point and the total number of risk types, obtain the probability of occurrence of the potential risk point; combine the affected conditions corresponding to the potential risk point in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk point is located, the probability of occurrence and the initial cost of the current project, to obtain the overall impact weight of the potential risk point on the project; use the affected conditions, probability of occurrence and overall impact weight corresponding to all potential risk points to determine the predicted risk cost of the current project.

[0042] Furthermore, the probability of occurrence of each potential risk point is evaluated based on the triggering cause of the risk type of the potential risk point and the number of risk types. The predicted risk cost of the current project is determined by combining the affected conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, and the initial cost of the current project.

[0043] This embodiment is described below using a potential risk point as an example, and other potential risk points can be processed using the method provided in this embodiment.

[0044] Specifically, for any potential risk point: calculate the inverse of the total number of risk types, record the inverse as the first occurrence probability, and the first occurrence probability is used to characterize the frequency of occurrence of the risk type of the potential risk point; calculate the inverse of the number of triggering reasons for the potential risk point, record the inverse as the second occurrence probability, and the second occurrence probability is used to characterize the probability of the potential risk point appearing in the corresponding risk type; take the product of the first occurrence probability and the second occurrence probability as the occurrence probability of the potential risk point. It should be noted that: one potential risk point corresponds to one risk triggering reason, so the number of triggering reasons is the number of potential risk points under this risk type.

[0045] For any potential risk point: calculate the average of the impact amounts corresponding to all historical risks of the potential risk point, and record it as the average impact amount; where the impact amount is the difference between the final cost of the project and the initial estimated cost; it should be noted that the initial estimated cost is the estimated cost when no risk is considered. The ratio between the average impact amount and the initial cost of the current project is recorded as the first ratio; the normalized result of the product of the similarity corresponding to the potential risk point, the average impact amount and the first ratio is determined as the overall impact weight of the potential risk point on the project. It should be noted that there are many methods for normalizing data. This embodiment adopts the maximum and minimum normalization method to normalize the product of the similarity, the average impact amount and the first ratio. The maximum and minimum normalization method is a prior art and will not be described in detail here. It should be noted that the initial cost of the current project is determined before the construction of the current project. The initial cost is a risk-free cost, that is, the cost determined when various risks are not considered.

[0046] By using the above method, the overall impact weight of each potential risk point on the project can be obtained.

[0047] After obtaining the overall impact weight of each potential risk point on the project, the risk cost of the current project is predicted by combining the average impact amount, occurrence probability and overall impact weight corresponding to all potential risk points.

[0048] Specifically, for any potential risk point, the product of the average impact amount, occurrence probability and overall impact weight corresponding to the potential risk point is calculated, and the product is used as the maximum impact value of the potential risk point. Using this method, the maximum impact value of each potential risk point can be obtained, and the cumulative sum of the maximum impact values ​​of all potential risk points is used as the predicted risk cost of the current project.

[0049] So far, this embodiment has determined the predicted risk cost of the current project.

[0050] Step S4, obtaining the predicted cost of the current project according to the initial cost of the current project and the predicted risk cost.

[0051] After determining the predicted risk cost of the current project, the sum of the initial cost of the current project and the predicted risk cost is taken as the predicted risk cost of the current project, thus completing the cost estimation of the current project.

[0052] This embodiment first divides the risk sequences under the same information source into multiple groups based on the similarities between the risk sequences in the historical risk information during the historical construction process. Each group is regarded as an initial risk point. A risk vector is constructed based on the frequency of occurrence of keywords in the initial risk points. The initial risk points are clustered into multiple target clusters based on the similarities between the risk vectors, and potential risk points are determined. These risk points are potential risk points that may be encountered in the current project. The potential laws and associations in the data are mined through grouping and clustering, and the historical risk information is summarized, which improves the accuracy of identifying potential risk points. Further, the risk cost of the current project is predicted by combining the triggering reasons of the risk types of the potential risk points, the total number of risk types, the affected conditions corresponding to the potential risk points in the historical risk information, and the initial cost of the current project. Then, the predicted cost of the current project is determined in combination with the initial cost of the current project. This embodiment constructs a risk database for each potential risk point, so that when performing potential risk analysis, the probability of occurrence of the potential risk point and the impact on the current project can be quickly and accurately output by matching the project type and content, thereby improving the efficiency and accuracy of engineering cost estimation.

[0053] An embodiment of a system for calculating the construction cost: See also Figure 2 , which shows a structural block diagram of a construction cost estimation system provided by an embodiment of the present invention. The system may include a data acquisition module, a first processing module, a second processing module and a prediction module.

[0054] Among them, the data collection module is used to obtain historical risk information during the historical construction process and construct various risk sequences based on the historical risk information; The first processing module is used to divide the risk sequences under the same information source into several groups according to the similarities between the risk sequences; construct risk vectors based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; cluster the initial risk points according to the similarities between the risk vectors to obtain several target clusters, potential risk points and risk types; The second processing module is used to obtain the probability of occurrence of potential risk points according to the triggering reasons of the risk types of potential risk points and the total number of risk types; combine the impact conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, the probability of occurrence and the initial cost of the current project to obtain the overall impact weight of the potential risk points on the project; use the impact conditions, probability of occurrence and overall impact weights corresponding to all potential risk points to determine the predicted risk cost of the current project; The prediction module is used to obtain the predicted cost of the current project based on the initial cost and predicted risk cost of the current project.

[0055] It should be understood that Figure 2 The structural block diagram of a system for calculating the cost of an engineering project and its modules shown in the figure can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented by using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0056] For more details about the above modules, please refer to other places in this manual and will not be repeated here.

[0057] In other embodiments, a device for calculating the construction cost is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the above-mentioned method for calculating the construction cost. The device can be a chip, a component or a module, and the chip can include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the method for calculating the construction cost provided in the above-mentioned embodiment.

[0058] In other embodiments, a computer program product is also provided. When the computer program product runs on a computer, the computer executes the above-mentioned related steps to implement a method for estimating the construction cost provided in the above-mentioned embodiment.

[0059] In other embodiments, a computer-readable storage medium is also provided, in which a computer program code is stored. When the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a method for calculating the construction cost provided in the above-mentioned embodiment.

[0060] Among them, the provided system, electronic device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0061] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating construction cost, characterized in that: The method comprises the following steps: Obtain historical risk information from historical construction processes and construct risk sequences based on the historical risk information; According to the similarity between risk sequences, the risk sequences under the same information source are divided into several groups; risk vectors are constructed based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; the initial risk points are clustered according to the similarity between risk vectors to obtain several target clusters, potential risk points and risk types; According to the triggering reasons of the risk types of potential risk points and the total number of risk types, the occurrence probability of potential risk points is obtained; the overall impact weight of potential risk points on the project is obtained by combining the impact conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, the occurrence probability and the initial cost of the current project; the predicted risk cost of the current project is determined by using the impact conditions, occurrence probability and overall impact weights corresponding to all potential risk points; The predicted cost of the current project is obtained based on the initial cost of the current project and the predicted risk cost.

2. A method for calculating construction cost according to claim 1, characterized in that: According to the similarities between risk sequences, the risk sequences under the same information source are divided into several groups, including: For any source: According to the quantity difference and DTW distance of the elements in the risk sequences of any two information sources, the similarity between the risk sequences of any two information sources is obtained, wherein the quantity difference and the DTW distance are negatively correlated with the similarity; A preset number of risk sequences are randomly selected as initial centers, and risk sequences whose similarity with the initial centers is greater than a preset similarity threshold are grouped together.

3. The method for calculating the construction cost according to claim 1, characterized in that: The step of constructing a risk vector based on the occurrence frequency of keywords in the initial risk point includes: Obtain all keywords in the candidate initial risk points; The vector composed of keywords whose occurrence frequency of all keywords in the candidate initial risk point is greater than the preset frequency threshold is used as the risk vector of the candidate initial risk point; The candidate initial risk point is any initial risk point.

4. A method for calculating construction cost according to claim 1, characterized in that: The initial risk points are clustered according to the similarities between the risk vectors to obtain several target clusters, potential risk points and risk types, including: A hierarchical clustering algorithm is used to cluster all initial risk points, and the average value of the similarities between all risk vectors in each cluster is used as the similarity degree corresponding to each cluster; The layer with the largest discrete degree of similarity corresponding to all clusters is taken as the final clustering result, and each target cluster in the final clustering result is obtained, and each target cluster is taken as a potential risk point; The next layer of the final clustering result is used as the risk type corresponding to each target cluster.

5. The method for calculating the construction cost according to claim 1, characterized in that: The occurrence probability of the potential risk point is obtained according to the triggering cause of the risk type of the potential risk point and the total number of risk types, including: For any potential risk point: Determining a first occurrence probability of a risk type of any potential risk point according to the total number of risk types; Determining a second occurrence probability of any potential risk point within a corresponding risk type according to the number of triggering causes of any potential risk point; The first occurrence probability and the second occurrence probability are combined to determine the occurrence probability of any potential risk point.

6. A method for calculating construction cost according to claim 4, characterized in that: The overall impact weight of the potential risk point on the project is obtained by combining the impact situation corresponding to the potential risk point in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk point is located, the probability of occurrence and the initial cost of the current project, including: For any potential risk point: Calculate the average of the impact amounts corresponding to all historical risks of the potential risk point, and record it as the average impact amount; where the impact amount is the difference between the final cost of the project and the initial estimated cost; calculating a first ratio between the average impact amount and the initial cost of the current project; The normalized result of the product of the similarity degree corresponding to any potential risk point, the average impact amount and the first ratio is determined as the overall impact weight of any potential risk point on the project.

7. A method for calculating construction cost according to claim 6, characterized in that: The predicted risk cost of the current project is determined by using the impact conditions, occurrence probability and overall impact weights corresponding to all potential risk points, including: For any potential risk point: the maximum impact value of any potential risk point is obtained by combining the average impact amount corresponding to any potential risk point, the probability of occurrence of any potential risk point and the overall impact weight of any potential risk point on the project; The cumulative sum of the maximum impact values ​​of all potential risk points is taken as the predicted risk cost of the current project.

8. The method for calculating construction cost according to claim 1, characterized in that: The method of obtaining the predicted cost of the current project based on the initial cost and predicted risk cost of the current project includes: The sum of the initial cost of the current project and the predicted risk cost is determined as the predicted cost of the current project.

9. A method for calculating construction cost according to claim 4, characterized in that: The obtaining of the discrete degree of the similarity degrees corresponding to all the clusters includes: taking the variance of the similarity degrees corresponding to all the clusters as the discrete degree.

10. A system for calculating construction cost, characterized in that: The system includes: The data collection module is used to obtain historical risk information during the historical construction process and construct various risk sequences based on the historical risk information; The first processing module is used to divide the risk sequences under the same information source into several groups according to the similarities between the risk sequences; construct risk vectors based on the frequency of occurrence of keywords in the initial risk points; each group is an initial risk point; cluster the initial risk points according to the similarities between the risk vectors to obtain several target clusters, potential risk points and risk types; The second processing module is used to obtain the probability of occurrence of potential risk points according to the triggering reasons of the risk types of potential risk points and the total number of risk types; combine the impact conditions corresponding to the potential risk points in the historical risk information, the overall similarity between the risk vectors in the target cluster where the potential risk points are located, the probability of occurrence and the initial cost of the current project to obtain the overall impact weight of the potential risk points on the project; use the impact conditions, probability of occurrence and overall impact weights corresponding to all potential risk points to determine the predicted risk cost of the current project; The prediction module is used to obtain the predicted cost of the current project based on the initial cost and predicted risk cost of the current project.

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