Science and technology project data management method and system for construction method revision process and medium

Abstract

The invention discloses a science and technology project data management method and system for a construction method revision process and a medium. Relates to the technical field of data management. According to the scheme, the construction method revision prediction model is trained according to the same-period rule change data and the revision process data, and construction method revision approval prediction is carried out according to the science and technology project data so as to guide the revision process of the target construction method; according to the scheme, science and technology project data management and construction method revision and approval processes are organically combined, so that the quality and traceability of construction method revision are improved; on the other hand, according to the scheme, synchronous rule change data and preprocessed revision process data are input into a machine learning network to train a construction method revision prediction model, so that historical construction method revision data are analyzed, the success rate of current construction method revision and possible problems are predicted, intelligent suggestions are provided for revision personnel, and the working efficiency is improved. The revision process is monitored in real time, and revision personnel are reminded of possible omission in time.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a method, system and medium for managing scientific and technological project data during a revision process of a construction method. Background Art

[0002] In the traditional process of scientific and technological project management, the data management of scientific and technological projects and the management of scientific research results (such as revision of construction methods and approval of construction methods) are usually two independent links. The data management of scientific and technological projects mainly involves the project establishment, implementation, acceptance and other stages, covering a large amount of technical data, experimental data, design drawings, construction records and other information; while the management of scientific research results focuses on the declaration, revision and approval of the construction methods, patents, papers and other results generated by the project. This separate management model has brought many problems in actual operation, especially in the process of revision or approval of construction methods, it is often necessary to reorganize and extract relevant data in scientific and technological projects, resulting in low work efficiency, increased duplication of work, and even the revision quality and approval efficiency of construction methods may be affected due to data omissions or errors.

[0003] First, the data of scientific and technological projects are usually scattered in different management systems or documents, lacking unified integration and standardized management. For example, technical data such as construction technology, material parameters, equipment configuration, etc. may be stored in different folders, databases or paper documents, and the revision or approval of construction methods requires the extraction of key information from these scattered data. This data extraction process is not only time-consuming and labor-intensive, but also prone to incomplete or inaccurate data due to human negligence, which in turn affects the revision quality and approval progress of construction methods.

[0004] Secondly, there is often information asymmetry between the data of scientific and technological projects and the requirements for revision or approval of construction methods. The revision or approval of construction methods has high requirements for the standardization, completeness and timeliness of data, and the data management of scientific and technological projects may not be optimized for these requirements. For example, the revision of construction methods requires detailed descriptions of technical solutions, process improvement instructions and experimental data support, while the data records of scientific and technological projects may lack systematic organization of these contents, resulting in repeated review and modification during revision, which increases the workload and time cost.

[0005] In addition, the traditional management model may also lead to delays in the revision and approval of construction methods. Since the data management of scientific and technological projects is out of touch with the revision and approval process of construction methods, extra time is often required for data collation and revision preparation after the project is completed, which not only prolongs the update cycle of construction methods, but may also miss some important approval windows. For example, during the approval process of construction methods, the approval department may require the latest technical data or experimental verification results, and if these data are not collated and updated in time, it may lead to approval delays or even rejection.

[0006] More importantly, the revision process of the construction method requires repeated review and modification of relevant technical data, which is extremely inefficient under the traditional management model. For example, the revision staff may need to review different versions of technical documents or experimental records multiple times to ensure the accuracy and consistency of the revised content. This repetitive work not only increases the workload, but may also lead to confusion in data versions, further affecting the quality and traceability of the construction method.

[0007] In summary, the traditional management mode of independent management of scientific and technological project data management and revision and approval of construction methods not only increases the workload, but also may lead to data quality problems and low approval efficiency. Therefore, there is an urgent need for a management method or system that can organically combine scientific and technological project data management with revision and approval processes of construction methods, so as to improve data utilization efficiency, reduce duplication of work, and ensure the timeliness and accuracy of revision and approval of construction methods. Summary of the invention

[0008] The technical problem to be solved by the present invention is that the traditional management mode of scientific and technological project data management and construction method revision and approval is independent of each other, which not only increases the workload, but also may lead to data quality problems and low approval efficiency; the purpose of the present invention is to provide a scientific and technological project data management method, system and medium for the construction method revision process, organically combine scientific and technological project data management with construction method revision and approval process, and improve the quality and traceability of construction method revision; on the other hand, this scheme also inputs the rule change data of the same period and the pre-processed revision process data into the machine learning network to train a construction method revision prediction model, so as to analyze the historical construction method revision data, predict the success rate of the current construction method revision and the problems that may be encountered, and provide intelligent suggestions for revision personnel, monitor the revision process in real time, and promptly remind revision personnel of possible omissions.

[0009] The present invention is achieved through the following technical solutions: This solution provides a scientific and technological project data management method for the revision process of the construction method, including: Collecting revision process data of historical construction methods and concurrent rule change data, and preprocessing the revision process data; the revision process data includes: revision data of construction methods and revision verification results; Input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train a method revision prediction model; Obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; The revision process of the target method is guided based on current science and technology project data and revised and approved forecast results.

[0010] A further optimization scheme is that the revision process data is preprocessed, including the following method: Extract the basic feature set related to the method approval results from the revision process data; Filter out multiple feature sets from the basic feature set based on different filtering models; Merge multiple feature sets to obtain a total feature set; Delete the duplicate features in the total feature set, and then calculate the correlation coefficient between each feature in the total feature set and the revised review of the working method. If the correlation coefficient calculated for the current feature does not reach the evaluation threshold, delete the current feature.

[0011] A further optimization scheme is that the calculation method of the correlation coefficient between each feature in the total feature set and the revision and approval of the working method includes: The correlation coefficient r between each characteristic and the revision and approval of the construction method is calculated according to the following formula: ; Where n represents the total number of revisions to the construction method; X k represents the kth revision of the construction method; Y k Indicates the review result of the kth revision of the construction method; represents the expectation of the kth revision of the construction method; Represents the expectation of the results of the k-th revision of the construction method review.

[0012] A further optimization scheme is that the pre-processed revision process data is input into a machine learning network to train a method revision prediction model; including the method: Perform quality inspection on the total feature set based on the rule change data of the same period and construct inspection features; Integrate the test features with the total feature set and divide them into a test set and a training set; The test set and training set are input into the XGboost model to train a method revision prediction model. During the training process, the parameters of the XGboost model are updated based on the optimization algorithm.

[0013] A further optimization scheme is to perform quality inspection on the total feature set based on the change status of the rules during the same period and construct inspection features; including the following methods: Extract all rule conditions from the rule change status of the same period; Searching for matching features of each rule condition in the feature set, and generating a matching feature set of each rule condition; Determine the quality of the total feature set based on the matching feature set of each rule condition: Determine whether the following formula is true: ; If it is established, the quality of the total feature set is judged to be excellent; otherwise, the quality of the total feature set is judged to be poor; where m represents the total number of features in the feature set; n represents the total number of rule conditions; S irepresents the number of matching features in the matching feature set of the i-th rule condition; c represents the number of matching feature sets whose number of matching features is less than c0; c0 represents the first matching feature number threshold; d represents the number of matching feature sets whose number of matching features is less than d0; d0 represents the second matching feature number threshold; c0>d0; Construct the test features from the total feature set with poor quality.

[0014] A further optimization scheme is that the inspection feature set is constructed based on the total feature set with poor quality, including the method: Acquire all rule conditions corresponding to the total feature set with poor quality to form a poor rule condition set and a supplementary feature set; the supplementary feature set is a difference set between the basic feature set and the total feature set; Perform a goodness of fit test on the supplementary features in the supplementary feature set and each difference rule condition: The goodness-of-fit test value X between supplementary feature i and structured rule j ij Calculated according to the following formula: ; Where N represents the goodness of fit coefficient; V ij represents the string matching degree between supplementary feature i and structured rule j; Z ij represents the cosine similarity between supplementary feature i and structured rule j; If the goodness-of-fit test value between the basic feature i and the difference rule condition j exceeds the preset goodness threshold Xe, the basic feature i is included in the test feature set.

[0015] A further optimization scheme is to update the parameters of the XGboost model based on the optimization algorithm; including the method: The parameters of the XGboost model are taken as the optimization object, and the improved whale optimization algorithm is initialized to calculate the fitness value of each whale. The parameters of the XGboost model are updated with the fitness value of each whale. The XGboost model is trained with the training set to predict the revision of the working method. The mean absolute percentage error of the prediction result of the revision of the working method is used as the fitness value of each whale for iteration to determine the optimal solution. The parameters include tree depth, learning rate, sample weight and sub-sample rate.

[0016] The further optimization scheme is that the iterative method of the improved whale optimization algorithm includes: Take L as the bracketing step length: ; in, represents the convergence factor; b represents a random number between [0, 1]; represents the position vector of the prey; represents the position vector of the whale in the tth iteration; Among them, the convergence factor converges in a nonlinear decreasing manner, and the decreasing form is: a=2·[1-cos(t / T max ) 2 ] ·(π / 2); a2 = (-1) · [1-cos(t / T max ) 2 ] ·(π / 2); Where t represents the current iteration number; T max Indicates the maximum number of iterations; like , then according to the formula Update the whale's position, otherwise press Update the whale's position; where, represents the position vector of the whale in the t+1th iteration; represents a randomly selected whale position vector.

[0017] This solution also provides a technology project data management system for the revision process of the construction method, which is used to implement the above-mentioned technology project data management method for the revision process of the construction method; the system includes: A preprocessing module is used to collect revision process data of historical construction methods and concurrent rule change data, and to preprocess the revision process data; the revision process data includes: revision data of construction methods and revision verification results; The model training module is used to input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train the method revision prediction model; The prediction module is used to obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; The guidance module is used to guide the revision process of the target method based on the current science and technology project data and the revision and approval forecast results.

[0018] The present invention provides a computer-readable medium having a computer program stored thereon, and the computer program is executed by a processor to implement the above-mentioned scientific and technological project data management method for the process of revision of the construction method.

[0019] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. The present invention provides a method, system and medium for managing scientific and technological project data for the revision process of construction methods; this solution organically combines scientific and technological project data management with the revision and approval process of construction methods to improve the quality and traceability of the revision of construction methods; on the other hand, this solution also inputs the rule change data of the same period and the pre-processed revision process data into the machine learning network to train a construction method revision prediction model, so as to analyze the historical construction method revision data, predict the success rate of the current construction method revision and the problems that may be encountered, and provide intelligent suggestions for the revision personnel, monitor the revision process in real time, and promptly remind the revision personnel of possible omissions; 2. The present invention provides a method, system and medium for managing scientific and technological project data in the process of revision of construction methods; this solution effectively combines the advantages of different screening models based on a hybrid feature selection method, and achieves more accurate classification with the help of the powerful generalization ability of the machine learning algorithm; at the same time, the parameters of the XGboost model are updated based on the optimization algorithm to quickly and effectively find the optimal parameters and improve the performance of the XGboost model. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 It is a flowchart of the scientific and technological project data management method used in the revision process of the construction method; Figure 2 Schematic diagram of the scientific and technological project data management system used in the process of revision of construction methods. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary implementation modes of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0022] The traditional management mode of independent data management, revision and approval of scientific and technological projects not only increases the workload, but also may lead to data quality problems and low approval efficiency. In view of this, this solution provides the following embodiments to solve the above technical problems.

[0023] Embodiment 1: This embodiment provides a technology project data management method for the process of revision of the construction method, such as Figure 1 As shown, including: Step 1: Collect the revision process data of the historical construction method and the rule change data of the same period, and pre-process the revision process data; the revision process data includes: the revision data of the construction method and the revision verification result; In this step, the construction method revision data mainly include: construction method revision technical parameters, revision times, interval time of each revision, approval process, approval departments involved, revision approval opinions, revision records and the review results of each revision, etc.; the concurrent rule change data mainly include the concurrent regulatory revision documents, industry standard revision documents, and relevant standard revision documents summarized based on big data, etc.

[0024] The revision process data is preprocessed, including the following method: S11, extracting the basic feature set related to the method approval results from the revision process data; S12, multiple feature sets are selected from the basic feature set based on different screening models; the screening models include the common random forest, mutual information method, L1 regularization method and sequential backward selection method. Generally, three screening models are selected to construct three feature sets; S13, merging multiple feature sets to obtain a total feature set; S14, delete the duplicate features in the total feature set, and then calculate the correlation coefficient between each feature in the total feature set and the revised review of the working method. If the calculated correlation coefficient of the current feature does not reach the evaluation threshold, delete the current feature.

[0025] The calculation method of the correlation coefficient between each feature in the total feature set and the revision and approval of the working method includes: The correlation coefficient r between each characteristic and the revision and approval of the construction method is calculated according to the following formula: ; Where n represents the total number of revisions to the construction method; X k represents the kth revision of the construction method; Y k Indicates the review result of the kth revision of the construction method; represents the expectation of the kth revision of the construction method; Represents the expectation of the results of the k-th revision of the construction method review.

[0026] Step 2: Input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train a method revision prediction model; this step specifically includes the following methods: S21, based on the rule change data of the same period, the quality of the total feature set is inspected and the inspection feature is constructed; this step specifically includes the following method: S211, extracting all rule conditions from the rule change status of the same period; S212, searching for matching features of each rule condition in the feature set, and generating a matching feature set of each rule condition; S213, judging the quality of the total feature set according to the matching feature set of each rule condition: judging whether the following formula is true: ; If it is established, the quality of the total feature set is judged to be excellent; otherwise, the quality of the total feature set is judged to be poor; where m represents the total number of features in the feature set; n represents the total number of rule conditions; S i represents the number of matching features in the matching feature set of the i-th rule condition; c represents the number of matching feature sets whose number of matching features is less than c0; c0 represents the first matching feature number threshold; d represents the number of matching feature sets whose number of matching features is less than d0; d0 represents the second matching feature number threshold; c0>d0; in this embodiment, c0 is 3 and d0 is 0; S214, constructing a test feature set based on the total feature set with poor quality. This step specifically includes the following method: S2141, obtaining all rule conditions corresponding to the total feature set with poor quality to form a poor rule condition set and a supplementary feature set; the supplementary feature set is a difference set between the basic feature set and the total feature set; S2142, perform a goodness of fit test on the supplementary features in the supplementary feature set and each difference rule condition: The goodness-of-fit test value X between supplementary feature i and structured rule j ij Calculated according to the following formula: ; Where N represents the goodness of fit coefficient; V ij represents the string matching degree between supplementary feature i and structured rule j; Z ij represents the cosine similarity between supplementary feature i and structured rule j; S2143, if the goodness-of-fit test value between the basic feature i and the difference rule condition j exceeds the preset goodness threshold value Xe, the basic feature i is included in the test feature set; S22, integrating the test feature set with the total feature set to divide them into a test set and a training set; combining the requirements of the rule change data of the same period to improve the accuracy of the features, so that the subsequent XGboost model can efficiently search for the best values ​​of these parameters, thereby improving the performance and accuracy of the model; S23, input the test set and the training set into the XGboost model to train a method revision prediction model, and update the parameters of the XGboost model based on the optimization algorithm during the training process.

[0027] The method of updating the parameters of the XGboost model based on the optimization algorithm includes: The parameters of the XGboost model are taken as the optimization object, and the improved whale optimization algorithm is initialized to calculate the fitness value of each whale. The parameters of the XGboost model are updated with the fitness value of each whale. The XGboost model is trained with the training set to predict the revision of the working method. The mean absolute percentage error of the prediction result of the revision of the working method is used as the fitness value of each whale for iteration to determine the optimal solution. The parameters include tree depth, learning rate, sample weight and sub-sample rate.

[0028] The iterative methods of the improved whale optimization algorithm include: Take L as the bracketing step length: ; in, represents the convergence factor; b represents a random number between [0, 1]; represents the position vector of the prey; represents the position vector of the whale in the tth iteration; Among them, the convergence factor converges in a nonlinear decreasing manner, and the decreasing form is: a=2·[1-cos(t / T max ) 2 ] ·(π / 2); a2 = (-1) · [1-cos(t / T max ) 2 ] ·(π / 2); Where t represents the current iteration number; T max Indicates the maximum number of iterations; like , then according to the formula Update the whale's position, otherwise press Update the whale's position; where, represents the position vector of the whale in the t+1th iteration; represents a randomly selected whale position vector.

[0029] The advantages of the XGboost model are its efficient computing power, powerful feature processing function, anti-overfitting performance, and excellent parallel design. However, its large number of parameters and complex initial settings are its major disadvantages; parameters such as tree depth, learning rate, sample weight, and sub-sample rate in the model will significantly affect the final performance. Although XGBoost contains regularization terms, overfitting may still occur if the hyperparameters are not set properly, especially on small data sets. Manually adjusting parameters is not only time-consuming, but also difficult to find the best combination. Therefore, the use of automated optimization algorithms helps to find the optimal parameters quickly and effectively, making parameter optimization a key factor affecting the performance of the algorithm. In order to ensure the effectiveness of the XGboost model, this solution uses an optimization algorithm to optimize the initial parameters.

[0030] This scheme is based on the improved whale optimization algorithm to optimize the initial parameters. The improved whale optimization algorithm improves the convergence factor for the prediction of the revision and approval results of the construction method, so that the convergence factor converges in a nonlinear decreasing manner, and can quickly and accurately predict the revision and approval results of the construction method.

[0031] Step 3: Obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; Step 4: Guide the revision process of the target method based on current science and technology project data and revised approval forecast results.

[0032] This embodiment organically combines the management of scientific and technological project data with the revision and approval process of construction methods to improve the quality and traceability of construction method revisions; on the other hand, this solution also inputs the rule change data of the same period and the pre-processed revision process data into the machine learning network to train a construction method revision prediction model to analyze the historical construction method revision data, predict the success rate of the current construction method revision and possible problems, and provide intelligent suggestions for revision personnel, monitor the revision process in real time, and promptly remind revision personnel of possible omissions.

[0033] Embodiment 2: This embodiment provides a technology project data management system for the process of revision of the construction method, which is used to implement the technology project data management method for the process of revision of the construction method described in Embodiment 1; Figure 2 As shown, the system comprises: A preprocessing module is used to collect revision process data of historical construction methods and concurrent rule change data, and to preprocess the revision process data; the revision process data includes: revision data of construction methods and revision verification results; The model training module is used to input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train the method revision prediction model; The prediction module is used to obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; The guidance module is used to guide the revision process of the target method based on the current science and technology project data and the revision and approval forecast results.

[0034] Embodiment 3: This embodiment provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the scientific and technological project data management method for the process of revision of the construction method as described in Embodiment 1, and specifically performs the following steps: Step 1: Collect the revision process data of the historical construction method and the rule change data of the same period, and pre-process the revision process data; the revision process data includes: the revision data of the construction method and the revision verification result; Step 2: Input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train a method revision prediction model; Step 3: Obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; Step 4: Guide the revision process of the target method based on current science and technology project data and revised approval forecast results.

[0035] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A scientific and technological project data management method for the revision process of a construction method, characterized in that: include: Collect the revision process data of historical construction methods and the rule change data of the same period, and pre-process the revision process data; The revision process data includes: method revision data and revision verification results; Input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train a method revision prediction model; Obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input them into the construction method revision prediction model to obtain the revision and approval prediction results; The revision process of the target method is guided based on current science and technology project data and revised and approved forecast results.

2. The scientific and technological project data management method for the process of revision of the construction method according to claim 1 is characterized in that: The revision process data is preprocessed, including the following method: Extract the basic feature set related to the method approval results from the revision process data; Filter out multiple feature sets from the basic feature set based on different filtering models; Merge multiple feature sets to obtain a total feature set; Delete the duplicate features in the total feature set, and then calculate the correlation coefficient between each feature in the total feature set and the revised review of the working method. If the correlation coefficient calculated for the current feature does not reach the evaluation threshold, delete the current feature.

3. The scientific and technological project data management method for the process of revision of the construction method according to claim 2 is characterized in that: The calculation method of the correlation coefficient between each feature in the total feature set and the revision and approval of the working method includes: The correlation coefficient r between each characteristic and the revision and approval of the construction method is calculated according to the following formula: ; Where n represents the total number of revisions to the construction method; X k represents the kth revision of the construction method; Y k Indicates the review result of the kth revision of the construction method; represents the expectation of the kth revision of the construction method; Represents the expectation of the results of the k-th revision of the construction method review.

4. The scientific and technological project data management method for the process of revision of the construction method according to claim 2 is characterized in that: The pre-processed revision process data is input into a machine learning network to train a method revision prediction model; Included methods: Perform quality inspection on the total feature set based on the rule change data of the same period and construct inspection features; Integrate the test features with the total feature set and divide them into a test set and a training set; The test set and training set are input into the XGboost model to train a method revision prediction model. During the training process, the parameters of the XGboost model are updated based on the optimization algorithm.

5. The scientific and technological project data management method for the process of revision of the construction method according to claim 4 is characterized in that: The quality inspection of the total feature set is performed based on the change status of the rules during the same period, and the inspection features are constructed; Included methods: Extract all rule conditions from the rule change status of the same period; Searching for matching features of each rule condition in the feature set, and generating a matching feature set of each rule condition; Determine the quality of the total feature set based on the matching feature set of each rule condition: Determine whether the following formula is true: ; If it is established, the quality of the total feature set is judged to be excellent; otherwise, the quality of the total feature set is judged to be poor; where m represents the total number of features in the feature set; n represents the total number of rule conditions; S i represents the number of matching features in the matching feature set of the i-th rule condition; c represents the number of matching feature sets whose number of matching features is less than c0; c0 represents the first matching feature number threshold; d represents the number of matching feature sets whose number of matching features is less than d0; d0 represents the second matching feature number threshold; c0>d0; Construct the test features from the total feature set with poor quality.

6. The method for managing scientific and technological project data in the process of revision of construction method according to claim 5, characterized in that: The method of constructing a test feature set based on a total feature set with poor quality includes: Acquire all rule conditions corresponding to the total feature set with poor quality to form a poor rule condition set and a supplementary feature set; the supplementary feature set is a difference set between the basic feature set and the total feature set; Convert all rule conditions into structured rules, and perform goodness of fit test between the supplementary features in the supplementary feature set and each structured rule: The goodness-of-fit test value X between supplementary feature i and structured rule j ij Calculated according to the following formula: ; Where N represents the goodness of fit coefficient; V ij represents the string matching degree between supplementary feature i and structured rule j; Z ij represents the cosine similarity between supplementary feature i and structured rule j; If the goodness-of-fit test value between the basic feature i and the difference rule condition j exceeds the preset goodness threshold Xe, the basic feature i is included in the test feature set.

7. The method for managing scientific and technological project data in the process of revision of construction methods according to claim 4, characterized in that: The parameters of the XGboost model are updated based on the optimization algorithm; Included methods: The parameters of the XGboost model are taken as the optimization object, and the improved whale optimization algorithm is initialized to calculate the fitness value of each whale. The parameters of the XGboost model are updated with the fitness value of each whale. The XGboost model is trained with the training set to predict the revision of the working method. The mean absolute percentage error of the prediction result of the revision of the working method is used as the fitness value of each whale for iteration to determine the optimal solution. The parameters include tree depth, learning rate, sample weight and sub-sample rate.

8. The scientific and technological project data management method for the process of revision of the construction method according to claim 7 is characterized in that: The iterative methods of the improved whale optimization algorithm include: Take L as the bracketing step length: ;in, represents the convergence factor; b represents a random number between [0, 1]; represents the position vector of the prey; represents the position vector of the whale in the tth iteration; Among them, the convergence factor converges in a nonlinear decreasing manner, and the decreasing form is: a=2· [1-cos(t / T max ) 2 ] ·(π / 2); a2 =(-1)· [1-cos(t / T max ) 2 ] ·(π / 2); Where t represents the current iteration number; T max Indicates the maximum number of iterations; like , then according to the formula Update the whale's position, otherwise press Update the whale's position; where, represents the position vector of the whale in the t+1th iteration; represents a randomly selected whale position vector.

9. A scientific and technological project data management system used in the revision process of construction methods, characterized in that: A method for managing scientific and technological project data for a process of revising a construction method according to any one of claims 1 to 8; the system comprises: A preprocessing module is used to collect revision process data of historical construction methods and concurrent rule change data, and to preprocess the revision process data; the revision process data includes: revision data of construction methods and revision verification results; The model training module is used to input the concurrent rule change data and the pre-processed revision process data into the machine learning network to train the method revision prediction model; The prediction module is used to obtain the revision data of the target construction method and the rule change data of the same period from the current scientific and technological project data, and input it into the construction method revision prediction model to obtain the revision and approval prediction results; The guidance module is used to guide the revision process of the target method based on the current science and technology project data and the revision and approval forecast results.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the scientific and technological project data management method for the process of revising a construction method as described in any one of claims 1 to 8.

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