Intelligent correction method for construction cost data of engineering site

Abstract

The invention relates to the technical field of engineering site cost, and discloses an intelligent correction method for engineering site cost data, which comprises the following steps: listing an engineering cost list, classifying the engineering cost list, carrying out anomaly detection, correcting a detected abnormal value, carrying out engineering construction and monitoring during construction, and calculating a judgment value P; engineering node progress monitoring and engineering equipment management are carried out during construction; according to the method, when data processing of the construction cost of the engineering site is carried out, firstly, the construction cost lists are classified, feature value extraction and dimension reduction processing are carried out during classification, and finally all the construction cost lists are changed into a feature set so that calculation can be conveniently carried out; abnormal value detection is carried out on the classified project cost list, abnormal values are detected in time and corrected, it is guaranteed that project cost data are accurate enough, project construction is carried out on the basis, and it is guaranteed that project construction can be carried out stably according to plans.

Description

Technical Field

[0001] The present invention relates to the technical field of project site cost, and more particularly to an intelligent correction method for project site cost data. Background Art

[0002] Project site cost is the process of carrying out project cost at the site. Project cost comprehensively applies knowledge and skills in aspects such as management science, economics, and engineering technology. Project cost is a work process of predicting, planning, controlling, accounting, analyzing, and evaluating construction projects. According to the procedures, methods, and bases stipulated by laws, regulations, and standards, the project price and its constituent contents are predicted or determined. Project cost includes engineering measurement and valuation standards, engineering valuation quotas, and project cost information related to valuation contents, valuation methods, and price standards; Project cost is a key factor in project construction, and its accuracy directly affects the budget and investment return of the project. However, due to many uncertain factors, such as changes in material costs, labor costs, machinery usage costs, etc., and design changes, deviations during the construction process, etc., project cost data may change throughout the project life cycle, which requires dynamic correction of the cost data to ensure its accuracy; When correcting the data of project cost, project cost includes various material lists, equipment lists, labor lists, and construction drawings, etc., and its data is numerous. Therefore, when correcting the data, a large amount of data needs to be processed. At this time, the time required for processing is relatively long, and when performing a large amount of data calculations, the performance requirements for the data processing device itself are relatively high, and it will increase the data processing loss; Nowadays, when carrying out project cost, after processing the project cost list in the early stage, the project construction is carried out according to the list at this time. Nowadays, when carrying out project construction, the progress of the project construction cannot be well supervised, and the project construction is easily affected by the own faults of the equipment. Now, the situation when the project equipment fails has not been considered in time, resulting in problems in the final project cost. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent correction method for project site cost data to solve the technical problems proposed in the background art.

[0004] To achieve the above object, the present invention provides the following technical solution: An intelligent correction method for project site cost data, comprising the following steps: Step S1, list the project cost list, calculate the reserve fund YB, and classify the project cost list; Step S2, perform anomaly detection on the classified project cost list, and correct the detected abnormal values; Step S3: Carry out the project construction according to the revised project cost list, and conduct real-time monitoring during the project construction; Step S4: Calculate the judgment value P during the project construction monitoring, judge whether the project construction is in a normal state based on the judgment value P, and go to the site for handling when the project construction is in an abnormal state; Step S5: Monitor the project node progress and manage the project equipment during the construction.

[0005] In a preferred embodiment, the specific steps of the said Step S1 are as follows: Step S101: Input all the list texts, and after input, perform word segmentation on all the project lists by using the word frequency statistics method; Step S102: After the word segmentation of the list text, it becomes a set of words, and the words represent the characteristics of their respective texts; Step S013: Extract the feature values from the set of words, and reduce the dimension of the text features by using the mutual information method; Step S104: Classify the list text after word segmentation and feature value extraction.

[0006] In a preferred embodiment, in the said Step S103, the mutual information is represented by the correlation degree MI between the words after word segmentation and the preset text categories, and the calculation formula of the correlation degree MI is , where Ci is the preset text category, word is the set of feature values after word segmentation, is the probability that the project list text belongs to the Ci text category and contains the feature value word, is the probability that all text categories contain the feature value word, is the probability that the text category belongs to the preset text category Ci. Extract the feature value word with the correlation degree MI higher than the preset threshold YY, and classify the feature value word into the text category Ci.

[0007] In a preferred embodiment, the specific process of the said Step S2 is as follows: Step S201: Conduct K-means clustering training and train out the clustering centers; Step S202: Use the trained clustering centers as labels and perform clustering on all the list texts; Step S203: When the list text is clustered, the list text can be classified into the clustering center, and the list text matches the clustering center, and this data is normal data; Step S204: When the list text cannot be classified into the clustering center, the list text does not match the clustering center, and this data is abnormal data; Step S205: Manually correct the abnormal data.

[0008] In a preferred embodiment, in step S201, when the K-means clustering method is trained, the first step is to initialize K clustering centers; the second step is to calculate the distance between each sample point and the clustering centers, and select the nearest clustering center as its classification until all samples are classified; the third step is to calculate the centroids of the K classes respectively as the new clustering centers, and return to the second step until the offset between the new centroid and the old centroid is less than the threshold, then end the algorithm and output all K clustering centers.

[0009] In a preferred embodiment, in step S3, during the real-time monitoring of engineering construction, collect the material usage data CL, project progress data JD, and project quality data ZL of the engineering construction and calculate the determination value P. The calculation formula of the determination value P is , where k1 and k2 are both weights, and 0 ≤ k1 ≤ 1, 0 ≤ k2 ≤ 1, k1 + k2 = 1. Calculate the determination value P once a day, and compare the calculated determination value P with the construction threshold CY. When the determination value P ≥ the construction threshold CY, the engineering construction is in a normal state at this time. When the determination value P < the construction threshold CY, the engineering construction is in an abnormal state at this time, and go to the construction site for processing.

[0010] In a preferred embodiment, in step S5, during the engineering construction, calculate the deviation value PL of the engineering construction. The calculation formula of the deviation value PL is , where N is the N engineering nodes divided by the overall engineering construction, i represents the current i-th engineering node, and pi is the difference between the actual construction progress of the current i-th engineering node and the theoretical progress of this stage.

[0011] In a preferred embodiment, compare the calculated deviation value PL with the progress threshold JY. When the deviation value PL > the progress threshold JY, the project needs to correct the data again. When the deviation value PL ≤ the progress threshold JY, the engineering construction continues according to the project cost list.

[0012] In a preferred embodiment, in step S5, for engineering equipment management, obtain the historical working data information of the engineering equipment. The historical working data information includes the total working time ZS of the equipment and the time GS when the equipment fails during work. And before the engineering equipment works, conduct quality inspection on it and generate a complete value ZY. The complete value is the brand-newness ratio BZ of the current engineering equipment relative to the new working equipment. Calculate the actual time SJ required for the equipment to work. The calculation formula of the actual time SJ is , where GZ is the theoretical working time required for the equipment to work. When it is a brand-new equipment, the theoretical working time GZ is directly used as the actual time SJ.

[0013] Technical effects and advantages of the present invention: 1. When processing the data of the project site cost in the present invention, the classification of the project cost list is first carried out. During the classification, eigenvalue extraction and dimensionality reduction processing are carried out. Finally, all the project cost lists are turned into a feature set, making it convenient for calculation. And after classification, the classified project cost list is subjected to outlier detection, and the outliers are detected in time and corrected to ensure that the project cost data is accurate enough. Then, based on the accurate project cost list, the project construction is carried out to ensure that the project construction can proceed steadily according to the plan; 2. After classifying the project cost list in the present invention, data detection is carried out in time, and the abnormal data in the data is corrected in time, thereby ensuring the accuracy of the project cost data. The K-means clustering method is used for outlier detection, which has the characteristics of simple operation and fast response, and can thus quickly detect the project cost list and improve the overall work efficiency; 3. The present invention collects three groups of data: the material usage data CL, the project progress data JD, and the project quality data ZL. The cooperation between the material usage and the project progress can, on the basis of knowing the current construction progress, understand whether the materials are well utilized and whether there is any waste of materials. And further, regarding the project quality, it can be understood whether the project built with the current materials meets the standards; 4. The present invention divides the entire project construction stage into N project nodes and manages the progress at each project node, which is convenient for accurately understanding it. And when the present application manages the progress of the project node, whether it is faster or slower than the project node, it only indicates that there is a problem with the project site cost data at this time. Therefore, when calculating the sum of the deviation values PL, the absolute value method is used for addition, and the calculated deviation value PL is accurate enough. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the intelligent correction method for the project site cost data of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The following will clearly and completely describe the technical solutions in the present invention in combination with the accompanying drawings. In addition, the forms of each structure described in the following embodiments are merely examples. An intelligent correction method for project site cost data involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0016] Referring to Figure 1 , the present invention provides an intelligent correction method for project site cost data, including the following steps: Step S1: List the project cost list, calculate the contingency reserve YB, and classify the project cost list; Step S2: Perform anomaly detection on the classified project cost list, and correct the detected abnormal values; Step S3: Carry out project construction in accordance with the corrected project cost list, and conduct real-time monitoring during project construction; Step S4: Calculate the judgment value P during project construction monitoring, judge whether the project construction is in a normal state based on the judgment value P, and go to the site for handling when the project construction is in an abnormal state; Step S5: Monitor the progress of project nodes and manage project equipment during construction.

[0017] In the embodiment of the present application, when processing the data of the project site cost, first classify the project cost list. During classification, eigenvalue extraction and dimensionality reduction processing will be carried out. Finally, all project cost lists will become a feature set, making it convenient for calculation. After classification, perform anomaly detection on the classified project cost list, detect abnormal values in a timely manner, and correct them to ensure the accuracy of project cost data. Then, based on the accurate project cost list, carry out project construction to ensure that the project construction can proceed steadily according to the plan. During construction, monitor it to ensure that any unreasonable places can be discovered in a timely manner during project construction, ensuring the smooth progress of project construction, and manage project equipment to avoid problems where the project cannot proceed smoothly when equipment fails.

[0018] Further, the specific steps for classifying the project cost list are as follows: Step S101: Input all list texts. After input, perform word segmentation processing on all project lists by means of word frequency statistics; Step S102: After the list text is word-segmented, it becomes a set of words, and the words represent the characteristics of their respective texts; Step S013: Extract eigenvalues from the set of words, and reduce the dimensionality of the text features by means of mutual information; Step S104: Classify the list text after word segmentation and eigenvalue extraction.

[0019] In the embodiment of the present application, the number of all list texts for project cost is large. Therefore, after all list texts are input, word segmentation is performed by means of word frequency. Since words, as combinations of Chinese characters, should appear stably in sentences, the more frequently several Chinese characters co-occur, the more likely these Chinese characters are to form a word. The word segmentation method using word frequency statistics in the present application is to count the frequency of combinations of Chinese characters appearing in the list text. For high-probability combinations of Chinese characters, they become a word, and this word can represent it, ensuring the accuracy of word segmentation. By using words to represent the characteristics of the list text, a large amount of text data processing is not required, and dimensionality reduction is performed. At this time, a large number of words can be classified, thereby improving the processing burden and reducing the processing difficulty.

[0020] Further, in step S103, eigenvalue extraction is performed on the set of words, and mutual information is used to reduce the dimensionality of text features. The mutual information is represented by the correlation MI between the words after word segmentation and the preset text categories. The calculation formula of the correlation MI is , where Ci is the preset text category, and word is the set of eigenvalues after word segmentation. is the probability that the engineering list text belongs to the Ci text category and contains the eigenvalue word. is the probability that all text categories contain the eigenvalue word. is the probability that the text category belongs to the preset text category Ci. Extract the eigenvalue word whose correlation MI is higher than the preset threshold YY, and classify the eigenvalue word into the text category Ci.

[0021] In the embodiment of the present application, after word segmentation, the list text becomes a set of a large number of words at this time. Each word in it represents the characteristics of the text it belongs to to a certain extent. If all words are directly processed, the subsequent processing speed will be affected due to the excessive feature dimension. Therefore, the present application uses mutual information for dimensionality reduction. The mutual information is to preset the text category in advance. At this time, the feature words after word segmentation are classified. Therefore, all eigenvalues after word segmentation can be classified into several corresponding categories, realizing the dimensionality reduction processing of the feature dimension. When subsequent operation processing is performed at this time, there is no need for more processing time due to more features, and the operation is more convenient and fast.

[0022] Further, in step S2, the classified project cost list is subjected to anomaly detection, and the detected abnormal values are corrected. The specific process is as follows: Step S201: Perform K-means clustering training and train the clustering center. Step S202: Use the trained clustering centers as labels and perform clustering on all the list texts. Step S203: When clustering the list texts, if a list text can be classified into a clustering center and matches the clustering center, this data is normal data. Step S204: When a list text cannot be classified into a clustering center and does not match the clustering center, this data is abnormal data. Step S205: Manually correct the abnormal data.

[0023] In the embodiment of the present application, after classifying the engineering cost list, at this time, only the engineering cost list texts are distinguished, but the data is not detected. Therefore, data detection needs to be carried out in a timely manner after classification, and the abnormal data in the data is corrected in a timely manner, so as to ensure the accuracy of the engineering cost data. The present application uses the K-means clustering method to detect abnormal data, which has the characteristics of simple operation and fast response, and can thus quickly detect the engineering cost list and improve the overall work efficiency. After the data detection in the present application, all the detected abnormal data are manually corrected to ensure that all the data is accurate enough when the construction project conducts the final project inspection, thus ensuring the project quality.

[0024] Further, in step S201, when the K-means clustering method is trained, the first step is to initialize K clustering centers; the second step is to calculate the distance between each sample point and the clustering centers, and select the nearest clustering center as its classification until all samples are classified; the third step is to calculate the centroids of the K classes respectively as the new clustering centers, and return to the second step until the offset between the new centroid and the old centroid is less than the threshold, then the algorithm ends and all K clustering centers are output.

[0025] In the embodiment of the present application, the clustering center method is adopted because the overall engineering cost list only fluctuates within a certain range, so it is difficult to detect abnormal data. However, in the K-means clustering method of the present application, during the preliminary training, different clustering centers will be trained according to the previous classification results. At this time, corresponding clustering centers will be generated relative to the classification results. Therefore, the method of one-to-one matching with different clustering centers is used to ensure that all classified engineering cost lists can be well processed and the accuracy of abnormal data detection is ensured.

[0026] Further, in the embodiment of the present application, in step S3, construction is carried out according to the corrected engineering cost list, and real-time monitoring is carried out during the construction. During the real-time monitoring of engineering construction, the material usage data CL, the project progress data JD, and the project quality data ZL of the engineering construction are collected, and the judgment value P is calculated. The calculation formula of the judgment value P is , where k1 and k2 are both weights, and 0 ≤ k1 ≤ 1, 0 ≤ k2 ≤ 1, k1 + k2 = 1. The judgment value P is calculated once a day, and the calculated judgment value P is compared with the construction threshold CY. When the judgment value P ≥ the construction threshold CY, the engineering construction is in a normal state at this time. When the judgment value P < the construction threshold CY, the engineering construction is in an abnormal state at this time, and it is necessary to go to the construction site for handling.

[0027] In the embodiment of the present application, when the project cost data are all corrected, the engineering construction work can be officially carried out. When carrying out the engineering construction, it is necessary to detect the state during the construction. The present application collects three groups of data: the material usage data CL, the project progress data JD, and the project quality data ZL. The cooperation between the material usage and the project progress can, on the basis of clearly understanding the current construction progress, understand whether the materials are well utilized and whether there is any waste of materials. Furthermore, regarding the project quality, it can be understood whether the project constructed based on the current materials meets the standards. Therefore, the judgment value P calculated by the present application using the above three groups of data can accurately represent the current situation of the project. The larger the judgment value, the better the construction quality and progress at this time. At this time, the judgment value P ≥ the construction threshold CY, and when the material usage data CL is high and the project progress data JD is low, there is a situation where the overall value is negative, indicating that there are relatively large problems in the construction at this time.

[0028] Further, in step S5, when carrying out the engineering construction, the deviation value PL of the engineering construction is calculated. The calculation formula of the deviation value PL is , where N is the N engineering nodes divided by the overall engineering construction, i represents the current i-th engineering node, and pi is the difference between the actual construction progress of the current i-th engineering node and the theoretical progress of this stage. The calculated deviation value PL is compared with the progress threshold JY. When the deviation value PL > the progress threshold JY, the project needs to be corrected again. When the deviation value PL ≤ the progress threshold JY, the engineering construction continues according to the project cost list.

[0029] In the embodiments of the present application, the overall construction period of the project is relatively long. In order to manage it precisely, the present application divides the entire project construction stage into N project nodes, and manages the progress one by one at each project node, which is convenient for accurately understanding it. Moreover, when the present application manages the progress of the project node, whether it is faster or slower than the project node, it only indicates that there is a problem with the on-site construction cost data at this time. Therefore, when the present application calculates the sum of the deviation values PL, it adds them in the form of their absolute values, and the calculated deviation value PL is accurate enough. When the deviation value PL > progress threshold JY, there are relatively large problems in the project construction at this time. Therefore, the project needs to be corrected again for the data.

[0030] Further, in step S5, engineering equipment management is carried out to obtain the historical working data information of the working equipment. The historical working data information includes the total working time ZS of the equipment and the time GS when the equipment fails during work. Moreover, the equipment is inspected before work, and a complete value ZY is generated. The complete value is the ratio BZ of the newness of the current engineering equipment to the new working equipment. Calculate the actual time SJ required for the equipment to work. The calculation formula for the actual time SJ is , where GZ is the theoretically required working time of the equipment. When it is a brand-new equipment, directly use the theoretically required working time GZ as the actual time SJ.

[0031] In the embodiments of the present application, during the actual project construction, the equipment will have a certain impact on the construction project. For example, when the equipment suddenly fails, it needs to be repaired at this time, and the repair time will waste the construction time. Therefore, the present application will calculate the actual time SJ. When calculating the actual time SJ, the time GS when the equipment fails during work and the total working time ZS of the equipment are used, taking into account the impact caused by the equipment failure, thereby ensuring the accuracy of correcting the on-site construction cost data. For example, the theoretically required working time for a piece of equipment to complete the project is 1000 hours, but since the equipment has worked for 1000 hours before, the time when it has problems is 20 hours, and its newness is 80%, the calculated actual time SJ is 1026 hours. The present application considers the failure time and finally ensures the accuracy of the project cost calculation.

[0032] Further, the calculation formula for the contingency reserve YB in step S1 is , where JB is the basic contingency reserve rate, GC is the project cost, QT is other project construction costs, n is the total annual project investment and construction fee, i is the current i-th year, TZi is the investment plan amount in the current i-th year, and SZ is the annual investment price increase rate.

[0033] In the embodiments of the present application, during engineering construction, the project reserve funds refer to the funds reserved to ensure the smooth progress of the project and are also an indispensable part of the project construction. The project reserve funds refer to the funds reserved to ensure the smooth progress of the project and are also an indispensable part of the project construction. During the construction process, various emergencies and problems may occur, such as construction progress delays caused by weather changes and temporary expenditures such as the need to replace damaged materials. The project reserve funds can be used to address these problems and ensure the smooth progress of the project. The present application accurately calculates the reserve funds to ensure the smooth progress of the project.

[0034] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0035] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0036] Finally: The above description is only the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent correction method for project site cost data, characterized in that: It includes the following steps: Step S1: List the project cost list, calculate the contingency reserve YB, and classify the project cost list; Step S2: Conduct anomaly detection on the classified project cost list and correct the detected outliers; Step S3: Carry out project construction according to the corrected project cost list and conduct real-time monitoring during project construction; Step S4: Calculate the judgment value P during project construction monitoring, judge whether the project construction is in a normal state based on the judgment value P, and go to the site for handling when the project construction is in an abnormal state; Step S5: Conduct project node progress monitoring and project equipment management during construction.

2. The intelligent correction method for project site cost data according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S101: Input all the list texts, and after input, perform word segmentation on all the project lists by using word frequency statistics; Step S102: After the word segmentation of the list text, it becomes a set of words, and the words represent the characteristics of the text they belong to; Step S013: Extract the feature values from the set of words and reduce the dimensionality of the text features by using mutual information; Step S104: Classify the list text after word segmentation and feature value extraction.

3. An intelligent correction method for project site cost data according to claim 2, characterized in that: In the step S103, the mutual information is represented by the correlation MI between the words after word segmentation and the preset text category, and the calculation formula of the correlation MI is , where Ci is the preset text category, and word is the set of eigenvalues after word segmentation, is the probability that the engineering list text belongs to the Ci text category and contains the eigenvalue word, is the probability that all text categories contain the eigenvalue word, is the probability that the text category belongs to the preset text category Ci. The eigenvalue word with the correlation MI higher than the preset threshold YY is extracted, and the eigenvalue word is classified into the text category Ci.

4. An intelligent correction method for engineering site cost data according to claim 1, characterized in that: The specific process of step S2 is as follows: Step S201: Conduct K-means clustering training and train out the clustering centers; Step S202: Use the trained clustering centers as labels and conduct clustering on all the list texts; Step S203: When clustering the list text, if the list text can be classified into the clustering center and the list text matches the clustering center, this data is normal data; Step S204: When the list text cannot be classified into the clustering center and the list text does not match the clustering center, this data is abnormal data; Step S205: Manually correct the abnormal data.

5. An intelligent correction method for project site cost data according to claim 4, characterized in that: In step S201, when the K-means clustering method is training, the first step: Initialize K clustering centers; The second step: Calculate the distance between each sample point and the clustering center, and select the nearest clustering center as its classification until all samples are classified; The third step: Calculate the centroids of the K classes respectively as the new clustering centers, and go back to the second step until the offset between the new centroid and the old centroid is less than the threshold, then end the algorithm and output all K clustering centers.

6. The intelligent correction method for engineering site cost data according to claim 1, characterized in that: In the step S3, during the real-time monitoring of the engineering construction, the material usage data CL, the project progress data JD, and the project quality data ZL of the engineering construction are collected, and a determination value P is calculated. The calculation formula of the determination value P is , where k1 and k2 are both weights, and 0≤k1≤1, 0≤k2≤1, k1 + k2 = 1. The determination value P is calculated once a day, and the calculated determination value P is compared with the construction threshold CY. When the determination value P≥the construction threshold CY, the engineering construction is in a normal state at this time. When the determination value P < the construction threshold CY, the engineering construction is in an abnormal state at this time, and it is necessary to go to the engineering site for processing.

7. An intelligent correction method for project site cost data according to claim 1, characterized in that: In the step S5, when the engineering construction is carried out, the deviation value PL of the engineering construction is calculated. The calculation formula of the deviation value PL is , where N is the N engineering nodes divided by the overall engineering construction, i represents the current i-th engineering node, and pi is the difference between the actual construction progress of the current i-th engineering node and the theoretical progress of this stage.

8. An intelligent correction method for project site cost data according to claim 6, characterized in that: Compare the calculated deviation value PL with the progress threshold JY. When the deviation value PL > progress threshold JY, the project needs to re-correct the data. When the deviation value PL ≤ progress threshold JY, the project construction continues according to the project cost list.

9. An intelligent correction method for project site cost data according to claim 1, characterized in that: In the step S5, engineering equipment management is carried out to obtain the historical working data information of the working equipment. The historical working data information includes the total working time ZS of the equipment and the time GS when faults occur during the equipment's work. And before the engineering equipment works, quality inspection is carried out on it, and a complete value ZY is generated. The complete value is the ratio BZ of the newness of the current engineering equipment to the new working equipment. Calculate the actual time SJ required when the equipment works. The calculation formula of the actual time SJ is , where GZ is the theoretically required working time of the equipment. When it is a brand-new equipment, directly take the theoretically required working time GZ as the actual time SJ.

10. An intelligent correction method for engineering site cost data according to claim 1, characterized in that: In the above step S1, the calculation formula for the contingency reserve YB is , where JB is the basic contingency reserve rate, GC is the project cost, QT is the other project construction costs, n is the total annual project investment and construction cost, i is the current year i, TZi is the investment plan amount for the current year i, and SZ is the annual investment price increase rate.

Images