Engineering project digital asset management platform

Abstract

The invention relates to the technical field of project datamation management, and discloses an engineering project digital asset management platform. The engineering project digital asset management platform comprises an acquisition module which is connected with an enterprise database to acquire project data of an enterprise; the classification module establishes a first item set and a second item set for the item data according to the item attributes and the work types; the analysis module determines the predicted working duration of each working type by using the data in the second project set; the uploading module is used for acquiring and transmitting a to-be-uploaded project official document; and the display module not only displays data in the first item set and the second item set according to the item attribute and the working type of the selected target, but also is used for acquiring the working type of the selected target and displaying the preset working duration of the working type in the second item set based on the working type. Through cooperative work of the acquisition module, the classification module, the analysis module, the uploading module and the display module, the data processing efficiency and accuracy are improved, and an effective management tool is provided for project management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of project data management, in particular to an engineering project digital asset management platform. BACKGROUND

[0002] Engineering project management aims to achieve systematic and standardized management of engineering project-related files. This includes but is not limited to file creation, secure storage, accurate classification, convenient sharing, version control, and final archiving, etc. A series of operation links. The fundamental purpose is to ensure the orderly management and efficient use of project files, so that team members can quickly and accurately obtain the latest files, thereby ensuring the security and integrity of the files and further improving the efficiency and quality of engineering project management.

[0003] Currently, the traditional engineering project data operation method mainly relies on human to collect, organize, analyze and apply data. Although this method is intuitive and easy to understand, it has many shortcomings in actual operation. First of all, human operation is often affected by personal subjective factors, which seriously challenges the accuracy and reliability of data processing. Secondly, human operation is low in efficiency when dealing with a large amount of data, which is difficult to meet the efficient demand of engineering project for data processing speed. Finally, human operation also has potential security risks such as data leakage or loss, which poses a threat to the smooth progress of engineering projects that cannot be ignored. SUMMARY

[0004] In view of this, the present application proposes an engineering project digital asset management platform to solve the problem of low data processing efficiency and high security risk of data leakage and loss caused by traditional engineering project data personnel operation in current technology.

[0005] The present application proposes an engineering project digital asset management platform, comprising:

[0006] An acquisition module is electrically connected to the database of an enterprise, and the acquisition module is used to acquire project data of the enterprise;

[0007] A classification module is electrically connected to the acquisition module, the classification module is used to acquire project attributes and work types of each project data, and the classification module is also used to establish a first project set according to the project attributes of each project data, and the classification module is also used to establish a second project set according to the work types of each project data;

[0008] An analysis module is electrically connected to the classification module, the analysis module is used to acquire each project data in the second project set, and determine the predicted work duration of each work type in the second project set according to the project data;

[0009] An uploading module electrically connected with the categorizing module, configured to acquire a project batch to be uploaded and transmit the project batch to the categorizing module;

[0010] A display module electrically connected with the analyzing module, configured to acquire a project attribute of a selected target and display project data in the first project set based on the project attribute, and acquire a work type of the selected target and display a preset work duration of the work type in the second project set based on the work type.

[0011] Further, the categorizing module is further configured to establish the first project set according to the project attribute, including:

[0012] The categorizing module is further configured to acquire a keyword in each of the project data and establish the first project set according to the keyword.

[0013] The categorizing module is further configured to remove duplicate data in the first project set.

[0014] The categorizing module is further configured to remove invalid data in the first project set after removing the duplicate data.

[0015] Further, the categorizing module is further configured to establish the second project set according to the work type, including:

[0016] The categorizing module is further configured to acquire each of the project data in the first project set after removing the invalid data and extract a keyword in each of the project data.

[0017] The categorizing module is further configured to determine the work type in each of the project data according to the keyword.

[0018] The categorizing module is further configured to establish a category label for each of the work types, categorize each of the project data according to the category label, and define the second project set according to each of the categorized project data.

[0019] Further, the analyzing module is configured to acquire each of the project data in the second project set and determine a predicted work duration of each of the work types in the second project set according to the project data, including:

[0020] The analyzing module is further configured to acquire an actual work time, a preset work time, a work quantity and a work total amount of each of the project data in each of the category labels.

[0021] The analysis module is further configured to determine a workload score of the project data according to the actual working time and the preset working time, and correct the workload score according to the number of workers and the total amount of work;

[0022] The analysis module is further configured to obtain a median of the workload scores of the project data after correction, and obtain an actual working time corresponding to the median of the workload scores;

[0023] The analysis module is further configured to obtain an actual working time axis of the project data according to linear regression, and determine a predicted working duration of the project data according to the actual working time axis;

[0024] The analysis module is further configured to compare the actual working time corresponding to the median of the workload scores with the predicted working duration, and determine whether the predicted working duration is accurate according to a comparison result;

[0025] When the actual working time corresponding to the median of the workload scores is less than or equal to the predicted working duration, the analysis module determines that the predicted working duration is accurate;

[0026] When the actual working time corresponding to the median of the workload scores is greater than the predicted working duration, the analysis module determines that the predicted working duration is inaccurate, and adjusts the actual working time axis until the actual working time corresponding to the median of the workload scores is less than or equal to the predicted working duration.

[0027] Further, when the analysis module determines the workload score of the project data according to the actual working time and the preset working time, the analysis module is further configured to:

[0028] The analysis module is further configured to obtain a time difference value between the actual working time and the preset working time of the project data, and determine the workload score of the project data according to a relationship between the time difference value and a preset time difference value;

[0029] The analysis module is configured with a first preset time difference value and a second preset time difference value, and the first preset time difference value is less than the second preset time difference value;

[0030] When the time difference value is less than or equal to the first preset time difference value, the analysis module determines that the workload score of the project data is M3;

[0031] When the time difference value is greater than the first preset time difference value and less than or equal to zero, the analysis module determines that the workload score of the project data is M2;

[0032] When the time difference value is greater than zero and less than or equal to the second preset time difference value, the analysis module determines the workload score of the project data as M1;

[0033] When the time difference value is greater than the second preset time difference value, the analysis module determines the workload score of the project data as M0;

[0034] And, M0

[0035] Further, the analysis module is further configured to determine the workload score of the project data as Mi, i = 0, 1, 2, 3, and correct the workload score according to the number of workers and the total amount of work, including:

[0036] The analysis module is further configured to obtain the average number of workers in each of the project data;

[0037] The analysis module is further configured to obtain the real-time number of workers in the project data, and obtain the number difference between the average number of workers and the real-time number of workers, and according to the adjustment coefficient of the adjustment of the workload score Mi of the project data between the number difference and the preset number difference.

[0038] Further, the analysis module is further configured to adjust the adjustment coefficient of the workload score Mi of the project data between the number difference and the preset number difference, including:

[0039] The analysis module is further configured with a first preset number difference and a second preset number difference, and the first preset number difference is less than the second preset number difference;

[0040] The analysis module is further configured to determine the adjustment coefficient of the workload score Mi of the project data according to the relationship between the number difference and each preset number difference:

[0041] When the number difference is less than or equal to the first preset number difference, the analysis module determines the adjustment coefficient as N3;

[0042] When the number difference is greater than the first preset number difference and less than or equal to zero, the analysis module determines the adjustment coefficient as N2;

[0043] When the number difference is greater than zero and less than or equal to the second preset number difference, the analysis module determines the adjustment coefficient as N1;

[0044] When the number difference is higher than the second preset number difference, the analysis module determines the adjustment coefficient as N0;

[0045] Wherein, 0.5

[0046] Further, the analysis module determines the adjustment coefficient as N i when adjusting the workload score Mi, i=0, 1, 2, 3, including:

[0047] The analysis module is further configured to obtain the average workload of each project data;

[0048] The analysis module is further configured to obtain the workload difference between the average workload and the total workload of the project data, and determine whether to correct the adjustment coefficient N i according to the workload difference;

[0049] When the workload difference is less than zero, the analysis module determines not to correct the adjustment coefficient N i;

[0050] When the workload difference is greater than or equal to zero, the analysis module corrects the adjustment coefficient N i according to the workload difference, and determines the correction parameter of the adjustment coefficient N i.

[0051] Further, when the analysis module corrects the adjustment coefficient N i according to the workload difference and determines the correction parameter of the adjustment coefficient N i, including:

[0052] The analysis module is further configured with a first preset workload difference and a second preset workload difference, and the first preset workload difference is less than the second preset workload difference;

[0053] The analysis module is further configured to determine the correction parameter of the adjustment coefficient N i according to the relationship between the workload difference and each preset workload difference;

[0054] When the workload difference is less than or equal to the first preset workload difference, the analysis module determines the correction parameter of the adjustment coefficient N i as B1;

[0055] When the workload difference is higher than the first preset workload difference and less than or equal to the second preset workload difference, the analysis module determines the correction parameter of the adjustment coefficient N i as B2;

[0056] When the workload difference is higher than the second preset workload difference, the analysis module determines the correction parameter of the adjustment coefficient N i as B3;

[0057] And, B1 B2 B3.

[0058] Compared with the prior art, the beneficial effects of the present application are that the acquisition module automatically acquires data of each project of the enterprise through electrical connection with the database of the enterprise. This automated process not only reduces the tediousness and possible errors of manual data entry, but also ensures the timeliness and accuracy of the data, thereby laying a solid foundation for subsequent data processing and analysis. Secondly, the classification module accurately classifies according to project attributes and work types, greatly improving the efficiency of data organization and retrieval. By establishing a first project set according to attributes and a second project set according to work types, the classification module makes data management more orderly. Systematic classification of different project data helps users quickly locate and obtain the required information, improving work efficiency and data utilization. Then, the analysis module provides a prediction of work duration by analyzing the data in the second project set. This function is of great significance for project planning and resource allocation. By analyzing the difference between actual working time and preset working time, managers can better understand the time requirements of each work type, thereby optimizing resource allocation, developing more reasonable project plans, and reducing the risk of resource waste and project delays. In addition, the upload module simplifies the management process of project documents. It not only acquires project documents to be uploaded, but also transmits these documents to the classification module for management. This function realizes the electronic and automated management of project documents, reduces the complexity and potential loss risk of paper file management, and improves the efficiency and security of project file management. Finally, the display module provides an intuitive user interface, allowing users to easily view and manage project data. By acquiring the project attributes of the selected target, the display module can display the project data in the first project set. At the same time, by acquiring the work type of the selected target, the display module can also show the preset working duration of each work type in the second project set. This visual display method allows managers to quickly understand project progress and resource usage, making more informed decisions. BRIEF DESCRIPTION OF DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference to the drawings. The drawings are for purposes of illustration only and are not considered a limitation of the present application. Moreover, like reference numerals are used to designate like parts throughout the specification and drawings. In the drawings:

[0060] Figure 1 The functional block diagram of the engineering project digital asset management platform provided by the embodiments of the present application. DETAILED DESCRIPTION

[0061] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood, and the scope of the present disclosure can be accurately conveyed to those skilled in the art. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0062] The project management aims to realize the systematic and standardized management of the project-related files. This includes but is not limited to the creation, secure storage, accurate classification, convenient sharing, version control and final archiving of the files, and a series of operation links. The fundamental purpose is to ensure the orderly management and efficient use of project files, so that team members can quickly and accurately obtain the latest files, thereby ensuring the security and integrity of the files, and further improving the efficiency and quality of the project management.

[0063] Currently, the traditional engineering project data operation mode mainly relies on manpower to collect, sort, analyze and apply data. Although this method is intuitive and easy to understand, it has many shortcomings in actual operation. First, human operation is often affected by personal subjective factors, which challenges the accuracy and reliability of data processing. Second, human operation is inefficient in processing large amounts of data, which is difficult to meet the efficient demand of engineering projects for data processing speed. Finally, human operation also has potential security risks such as data leakage or loss, which poses a threat to the smooth progress of the engineering project.

[0064] In view of this, the present application provides an engineering project digital asset management platform to solve the problem of low data processing efficiency and high security risk of data leakage and loss caused by personnel operation in traditional engineering project data.

[0065] As Figure 1As shown, in some embodiments of the present application, the present embodiment provides an engineering project digital asset management platform, comprising: an acquisition module, a classification module, an analysis module, an upload module and a display module. The acquisition module is electrically connected with the database of the enterprise, and the acquisition module is used to acquire the project data of the enterprise; the classification module is electrically connected with the acquisition module, and the classification module is used to acquire the project attribute and the work type of each project data, and the classification module is also used to establish a first project set according to the project attribute, and the classification module is also used to establish a second project set according to the work type; the analysis module is electrically connected with the classification module, and the analysis module is used to acquire each project data in the second project set, and determine the predicted working time of each work type in the second project set according to the project data; the upload module is electrically connected with the classification module, and the upload module is used to acquire the project batch text to be uploaded, and transmit the project batch text to the classification module; the display module is electrically connected with the analysis module, and the display module is used to acquire the project attribute of the selected target, and display the project data in the first project set based on the project attribute, and the display module is also used to acquire the work type of the selected target, and display the preset working time of the work type in the second project set based on the work type.

[0066] Specifically, the acquisition module serves as the entry point for data, electrically connected to the enterprise's database, ensuring real-time acquisition and updating of project data. Through this module, the system can automatically extract the required project data from the database, including project attributes, work types, progress information, etc. In this way, not only does it reduce the workload and error rate of manual data entry, but it also ensures the timeliness and accuracy of the data, providing a reliable foundation for subsequent data processing and analysis. Secondly, the classification module is electrically connected to the acquisition module and is responsible for classifying and managing the acquired project data. This module first establishes the data as a first project set based on project attributes, and then establishes a second project set based on work types. Through this systematic classification method, the classification module effectively organizes a large amount of project data, making data management and retrieval more efficient and orderly. For example, different project attributes may correspond to different project stages, departments, or customers, while work types may involve design, construction, supervision, and other specific work content. This classification method not only facilitates data management but also provides a clear structure for subsequent analysis. The analysis module is electrically connected to the classification module and is mainly responsible for in-depth analysis of the data in the second project set. Through the analysis module, the system can obtain detailed data for each work type and predict the work duration for each work type based on these data. Specifically, the analysis module may use statistical analysis, machine learning, and other technical means to consider historical data and current project conditions to give reasonable work duration predictions. This function is of great significance to project management as it can help project managers more accurately plan time and allocate resources, avoiding project delays and resource waste. In addition, the upload module is responsible for the management of project documents. This module is electrically connected to the classification module and can automatically obtain project documents to be uploaded and transmit them to the classification module for archiving and management. Through this process, the upload module realizes the electronic management of project documents, simplifying the traditional paper file process and improving the efficiency and security of file management. In addition, electronic management facilitates quick retrieval and sharing of documents, further enhancing the convenience of project management. Finally, the display module is electrically connected to the analysis module and provides a user-friendly interface that allows users to intuitively view and manage project data. The display module can obtain the project attributes of the selected target and display the relevant data in the first project set based on these attributes. At the same time, the display module can also obtain the work type of the selected target and display the corresponding preset work duration in the second project set. Through this intuitive display method, users can quickly understand the progress of the project and the use of resources, making more informed decisions. This visualization function not only improves the readability of data but also enhances the user's operation experience.

[0067] It can be understood that through the cooperative work of the acquisition module, the classification module, the analysis module, the uploading module and the display module, efficient management and utilization of project data are realized. It not only improves the efficiency and accuracy of data processing, optimizes resource allocation, but also provides a powerful support tool for project management of enterprises, which helps the smooth implementation and successful delivery of projects.

[0068] In some embodiments of the present application, the classification module is further configured to establish the first project set according to the project attributes, including: the classification module is further configured to acquire the keywords in the project data, and establish the first project set according to the keywords; the classification module is further configured to remove the duplicate data in the first project set; the classification module is further configured to remove the invalid data in the first project set after removing the duplicate data.

[0069] Specifically, the classification module acquires the keywords in the project data, and uses natural language processing technology or keyword extraction algorithm to extract the keywords from the project description, title or other related information. These keywords can be project attribute-related words such as project phase, geographical location, project type, and work type-related words such as design, construction, and testing. Through keyword extraction, the classification module can more accurately classify each project data into the corresponding project attribute and work type, thereby establishing the first project set. Secondly, the classification module also uses deduplication technology to remove duplicate data in the first project set. By comparing the key information of the project data such as project name, number, keywords, etc., the classification module can identify and remove duplicate project data, ensuring the uniqueness and accuracy of the data in the first project set. Finally, the classification module also cleans up the first project set by removing invalid data. Invalid data refers to data that does not meet the project attribute or work type, or project information that is missing or incomplete. By identifying and removing these invalid data, the classification module can ensure the quality and availability of the data in the first project set, providing a reliable data foundation for subsequent data analysis and management.

[0070] In some embodiments of the present application, the classification module is further configured to establish the second project set according to the work type, including: the classification module is further configured to acquire the project data in the first project set after removing the invalid data, and extract the keywords in the project data; the classification module is further configured to determine the work type in each project data according to the keywords; the classification module is further configured to establish a category label for each work type, and classify each project data according to the category label, and define the classified project data as the second project set.

[0071] Specifically, the categorization module obtains each item data in the first item set after removing the invalid data, and extracts keywords in each item data. Using natural language processing technology or keyword extraction algorithm, the categorization module extracts keywords from the project description, title or other related information. These keywords can represent the characteristics or content of each project, which is helpful for subsequent work type determination and categorization operation. Secondly, the categorization module determines the work type in each item data according to the keywords. By defining the work type list or classification standard in advance, the categorization module matches the extracted keywords with the work type to determine the work type to which each project belongs. Through the keyword matching algorithm or machine learning model, the work type is effectively determined according to the semantic and contextual information of the keywords. Finally, the categorization module establishes a category label for each work type, and categorizes each item data according to the category label, and then defines a second item set according to the categorized item data. By establishing a category label for each work type, the categorization module can better organize and manage the project data, so that the second item set has a clear structure and classification.

[0072] In some embodiments of the present application, when the analysis module is used to obtain each item data in the second item set, and determine the predicted work duration of each work type in the second item set according to the item data, it comprises: the analysis module is also used to obtain the actual work time, the preset work time, the work number and the work total amount of each item data in each category label; the analysis module is also used to determine the work load score of the item data according to the actual work time and the preset work time, and correct the work load score according to the work number and the work total amount; the analysis module is also used to obtain the median of the corrected work load score of each item data, and obtain the actual work time corresponding to the median of the work load score; the analysis module is also used to obtain the actual work time axis of each item data according to linear regression, and determine the predicted work duration of each item data according to the actual work time axis; the analysis module is also used to compare the actual work time corresponding to the median of the work load score with the predicted work duration, and determine whether the predicted work duration is accurate according to the comparison result, wherein: when the actual work time corresponding to the median of the work load score is less than or equal to the predicted work duration, the analysis module determines that the predicted work duration is accurate. When the actual work time corresponding to the median of the work load score is greater than the predicted work duration, the analysis module determines that the predicted work duration is not accurate, and adjusts the actual work time axis until the actual work time corresponding to the median of the work load score is less than or equal to the predicted work duration.

[0073] Specifically, the analysis module obtains the actual working time, the preset working time, the number of workers and the total amount of work of each item data in various categories. These data are important indicators of project execution, and by comparing the actual and preset working time, the number of workers and the total amount of work, the analysis module can understand the actual situation of project execution and provide basic data for subsequent work duration prediction. Secondly, the analysis module determines the work load score of the project data according to the actual working time and the preset working time, and corrects the work load score according to the number of workers and the total amount of work. The work load score reflects the workload and difficulty of project execution, and by considering the number of workers and the total amount of work, the analysis module can more accurately assess the work load of the project and provide a more accurate basis for subsequent prediction of work duration. Next, the analysis module obtains the median of the corrected work load score of each item data, and obtains the actual working time corresponding to the median of the work load score. This step obtains the median of the work load score through statistical analysis of the work load score, and calculates the actual working time accordingly, providing a reference for subsequent prediction of work duration. Then, the analysis module obtains the actual working time axis of each item data according to linear regression, and determines the predicted work duration of each item data according to the actual working time axis. Using the linear regression model, the analysis module can predict the trend of the actual working time, thereby calculating the future work duration and providing a basis for project progress prediction. Finally, the analysis module determines whether the predicted work duration is accurate according to the comparison between the actual working time corresponding to the median of the work load score and the predicted work duration. By comparing the actual working time and the predicted work duration, the analysis module can evaluate the accuracy of the prediction, and adjust the actual working time axis as needed until the accuracy requirement of the predicted work duration is met.

[0074] In some embodiments of the present application, when the analysis module determines the work load score of the project data according to the actual working time and the preset working time, the analysis module is further configured to: obtain the time difference value between the actual working time and the preset working time of the project data, and determine the work load score of the project data according to the relationship between the time difference value and the preset time difference value; wherein the analysis module is configured with a first preset time difference value and a second preset time difference value, and the first preset time difference value is less than the second preset time difference value; when the time difference value is less than or equal to the first preset time difference value, the analysis module determines the work load score of the project data as M3; when the time difference value is greater than the first preset time difference value and less than or equal to zero, the analysis module determines the work load score of the project data as M2; when the time difference value is greater than zero and less than or equal to the second preset time difference value, the analysis module determines the work load score of the project data as M1; when the time difference value is greater than the second preset time difference value, the analysis module determines the work load score of the project data as M0; and M0M1M2M3.

[0075] Specifically, the analysis module obtains a time difference value between the actual working time and the preset working time of the project data. This step calculates the time difference value by comparing the actual completion time and the preset completion time of the project, which reflects the difference between the actual execution of the project and the planned expectation. Then, the analysis module determines the workload score of the project data according to the relationship between the time difference value and the preset time difference value. Here, an evaluation standard based on the time difference value is adopted, which reflects the execution of the project and the degree of workload by setting different time difference value ranges and corresponding workload score levels. Specifically, the analysis module configures a first preset time difference value and a second preset time difference value, and determines the workload score of the project data according to the comparison result of the time difference value and the two preset time difference values. When the time difference value is less than or equal to the first preset time difference value, it is rated as M3 level, indicating that the project execution is good; when the time difference value is greater than the first preset time difference value and less than or equal to zero, it is rated as M2 level, indicating that the project is slightly delayed but still within an acceptable range; when the time difference value is greater than zero and less than or equal to the second preset time difference value, it is rated as M1 level, indicating that the project has a certain degree of delay; and when the time difference value is greater than the second preset time difference value, it is rated as M0 level, indicating that the project execution is significantly delayed.

[0076] In some embodiments of the present application, the analysis module is further configured to determine the workload score of the project data as Mi, i = 0, 1, 2, 3, and correct the workload score according to the number of workers and the total amount of work. The analysis module is further configured to obtain the average number of workers in each project data; the analysis module is further configured to obtain the real-time number of workers in the project data, and obtain the number difference value between the average number of workers and the real-time number of workers, and determine the adjustment coefficient of the workload score Mi of the project data when adjusting according to the number difference value and the preset number difference value.

[0077] In some embodiments of the present application, the analysis module is further configured to determine the adjustment coefficient of the workload score M i of the project data when adjusting according to the difference between the number of people and the preset number of people difference, comprising: the analysis module is further configured with a first preset number of people difference and a second preset number of people difference, and the first preset number of people difference is less than the second preset number of people difference; the analysis module is further configured to determine the adjustment coefficient of the workload score M i of the project data when adjusting according to the relationship between the number of people difference and each preset number of people difference: when the number of people difference is less than or equal to the first preset number of people difference, the analysis module determines the adjustment coefficient as N3; when the number of people difference is greater than the first preset number of people difference and the number of people difference is less than or equal to zero, the analysis module determines the adjustment coefficient as N2; when the number of people difference is greater than zero and the number of people difference is less than or equal to the second preset number of people difference, the analysis module determines the adjustment coefficient as N1; when the number of people difference is greater than the second preset number of people difference, the analysis module determines the adjustment coefficient as N0; wherein 0.5

[0078] Specifically, the analysis module obtains the average number of workers in each project data. This average value can be obtained by statistical analysis of historical project data or data within a certain period of time, reflecting the average level of work in the project, which serves as a reference for subsequent workload scoring. Secondly, the analysis module obtains the real-time number of people and calculates the number of people difference between the average number of workers and the real-time number of people. This difference can reflect the change of human resources in the current project execution process, that is, the difference between the actual situation and the average level. Next, the analysis module determines the adjustment coefficient N i of the workload score M i of the project data according to the relationship between the number of people difference and the preset number of people difference. The analysis module determines the adjustment coefficient N i corresponding to different number of people difference ranges according to the preset number of people difference range (the first preset number of people difference and the second preset number of people difference). When the number of people difference is in different preset ranges, the workload score is adjusted according to the preset adjustment coefficient N. This method can dynamically adjust the workload score according to the change of human resources, and more accurately reflect the actual situation of the project. Finally, the analysis module corrects the workload score according to the adjustment coefficient N i. According to the given range and adjustment coefficient N i, the analysis module can ensure that the adjustment of the workload score is within a reasonable range, so as to more accurately reflect the situation of project execution. This dynamic adjustment method can make the workload score more objective and reliable, which helps project managers better allocate resources and control progress.

[0079] In some embodiments of the present application, when the analysis module determines the adjustment factor N i of the workload score Mi, i = 0, 1, 2, 3, the analysis module is further configured to obtain the average workload of each project data; the analysis module is further configured to obtain the workload difference between the average workload and the total workload of the project data, and determine whether to correct the adjustment factor N i according to the workload difference; when the workload difference is less than zero, the analysis module determines that the adjustment factor N i does not need to be corrected; when the workload difference is greater than or equal to zero, the analysis module corrects the adjustment factor N i according to the workload difference, and determines the correction parameter of the adjustment factor N i.

[0080] In some embodiments of the present application, when the analysis module corrects the adjustment factor N i according to the workload difference, and determines the correction parameter of the adjustment factor N i, the analysis module is further configured with a first preset workload difference and a second preset workload difference, and the first preset workload difference is less than the second preset workload difference; the analysis module is further configured to determine the correction parameter of the adjustment factor N i according to the relationship between the workload difference and each preset workload difference; when the workload difference is less than or equal to the first preset workload difference, the analysis module determines that the correction parameter of the adjustment factor N i is B1; when the workload difference is greater than the first preset workload difference and less than or equal to the second preset workload difference, the analysis module determines that the correction parameter of the adjustment factor N i is B2; when the workload difference is greater than the second preset workload difference, the analysis module determines that the correction parameter of the adjustment factor N i is B3; and B1 < B2 < B3.

[0081] Specifically, the analysis module obtains the average work amount of each project data. This average work amount can be obtained by statistical analysis of historical project data or data within a certain period of time, which reflects the usual work load level in the project as a reference for subsequent work load scoring. Secondly, the analysis module obtains the work amount difference between the average work amount and the total work amount of the project data, and determines whether to modify the adjustment coefficient N i according to the work amount difference. The work amount difference reflects the difference between the actual work amount and the expected work amount, which can be used to judge the project execution by comparison. Next, the analysis module determines the modification parameter of the adjustment coefficient N i according to the relationship between the work amount difference and the preset work amount difference. The analysis module configures a first preset work amount difference and a second preset work amount difference, and determines the adjustment parameter B i corresponding to different work amount difference ranges according to the comparison result of the work amount difference and the two preset work amount differences. When the work amount difference is within different preset ranges, the adjustment coefficient N i is modified according to the preset modification parameter B i. This method can dynamically control the adjustment of the work load score according to the change of the work amount, making it more accurately reflect the actual situation of the project execution. Finally, the adjustment coefficient N i is modified according to the adjustment parameter B i. According to the given range and the modification parameter B i, the analysis module can ensure that the adjustment of the adjustment coefficient N i is within a reasonable range, so as to more accurately reflect the situation of the project execution and the change of the work load. This dynamic adjustment method can further make the work load score more objective and reliable, which is helpful for the project manager to better allocate resources and control progress.

[0082] In the above embodiments, the acquisition module automatically acquires data of each project of the enterprise by being electrically connected with the database of the enterprise. This automated process not only reduces the tediousness and possible errors of manual data entry, but also ensures the timeliness and accuracy of the data, thereby laying a solid foundation for subsequent data processing and analysis. Secondly, the classification module accurately classifies the data according to the project attributes and work types, greatly improving the efficiency of data organization and retrieval. By establishing a first project set according to the attributes and a second project set according to the work types, the classification module makes data management more orderly. Systematic classification of different project data helps users quickly locate and obtain the required information, improving work efficiency and data utilization. Then, the analysis module provides a prediction of the work duration by analyzing the data in the second project set. This function is of great significance for project planning and resource allocation. By analyzing the difference between the actual work time and the preset work time, managers can better understand the time requirements of each work type, thereby optimizing resource allocation, developing more reasonable project plans, and reducing the risk of resource waste and project delays. In addition, the upload module simplifies the management process of project documents. It not only acquires the project documents to be uploaded, but also transmits these documents to the classification module for management. This function realizes the electronic and automated management of project documents, reduces the complexity and potential loss risk of paper file management, and improves the efficiency and security of project file management. Finally, the display module provides an intuitive user interface, allowing users to easily view and manage project data. By acquiring the project attributes of the selected target, the display module can display the project data in the first project set. At the same time, by acquiring the work type of the selected target, the display module can also display the preset work duration of each work type in the second project set. This visual display method allows managers to quickly understand the project progress and resource usage, making more informed decisions.

[0083] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.

[0084] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0085] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0086] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0087] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the technical solutions of the present application. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or equivalent replacements without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A digital asset management platform for engineering projects, characterized in that, include: The acquisition module is electrically connected to the enterprise's database, and the acquisition module is used to acquire data for various projects of the enterprise. A classification module is electrically connected to the acquisition module. The classification module is used to acquire the project attributes and work types of each project data. The classification module is also used to establish a first project set for each project data according to the project attributes. The classification module is also used to establish a second project set for each project data according to the work types. An analysis module, electrically connected to the classification module, is used to acquire data for each item in the second project set and determine the predicted working time for each work type in the second project set based on the project data. An upload module is electrically connected to the classification module. The upload module is used to obtain the project approval document to be uploaded and transmit the project approval document to the classification module. The display module is electrically connected to the analysis module. The display module is used to obtain the project attributes of the selected target and display the project data in the first project set based on the project attributes. The display module is also used to obtain the work type of the selected target and display the preset working time of the work type in the second project set based on the work type.

2. The digital asset management platform for engineering projects as described in claim 1, characterized in that, The classification module is further configured to, when establishing a first item set for each item data according to the item attributes, include: The classification module is also used to obtain keywords in each of the project data, and to establish a first project set for each of the project data based on the keywords; The classification module is also used to remove duplicate data from the first item set; The classification module is also used to remove invalid data from the first item set after removing duplicate data.

3. The digital asset management platform for engineering projects as described in claim 2, characterized in that, The classification module is further configured to, when establishing a second itemset for each of the project data according to the work type, include: The classification module is also used to obtain the data of each item in the first item set after removing the invalid data, and to extract keywords from each item data; The classification module is also used to determine the job type in each of the project data based on the keywords; The classification module is also used to establish category labels for each of the work types, classify each of the project data according to the category labels, and define each of the classified project data as the second project set.

4. The digital asset management platform for engineering projects as described in claim 3, characterized in that, The analysis module is used to acquire data for each project in the second project set, and to determine the predicted work duration for each work type in the second project set based on the project data, including: The analysis module is also used to obtain the actual working time, preset working time and number of workers and total workload of each item data in each of the aforementioned categories of labels; The analysis module is also used to determine the workload score of the project data based on the actual working time and the preset working time, and to correct the workload score based on the number of workers and the total workload; The analysis module is also used to obtain the median of the workload score after the data of each project is corrected, and to obtain the actual working time corresponding to the median of the workload score; The analysis module is also used to obtain the actual working time axis of each project data according to linear regression, and to determine the predicted working time of each project data according to the actual working time axis. The analysis module is also used to compare the actual working time corresponding to the median of the workload score with the predicted working time, and to determine whether the predicted working time is accurate based on the comparison result, wherein: When the actual working time corresponding to the median of the workload score is less than or equal to the predicted working time, the analysis module determines that the predicted working time is accurate. When the actual working time corresponding to the median of the workload score is greater than the predicted working time, the analysis module determines that the predicted working time is inaccurate and adjusts the actual working time axis until the actual working time corresponding to the median of the workload score is less than or equal to the predicted working time.

5. The digital asset management platform for engineering projects as described in claim 4, characterized in that, The analysis module is also used to determine the workload score of the project data based on the actual working time and the preset working time, including: The analysis module is also used to obtain the time difference between the actual working time and the preset working time of the project data, and to determine the workload score of the project data based on the relationship between the time difference and the preset time difference. The analysis module is configured with a first preset time difference and a second preset time difference, wherein the first preset time difference is less than the second preset time difference. When the time difference is less than or equal to the first preset time difference, the analysis module determines the workload score of the project data to be M3. When the time difference is greater than the first preset time difference and the time difference is less than or equal to zero, the analysis module determines the workload score of the project data to be M2. When the time difference is greater than zero and the time difference is less than or equal to the second preset time difference, the analysis module determines the workload score of the project data to be M1. When the time difference is greater than the second preset time difference, the analysis module determines that the workload score of the project data is M0. Furthermore, M0 < M1 < M2 < M3.

6. The digital asset management platform for engineering projects as described in claim 5, characterized in that, The analysis module is also used to determine the workload score of the project data as Mi, i = 0, 1, 2, 3, and to correct the workload score based on the number of workers and the total workload, including: The analysis module is also used to obtain the average number of employees in each of the project data; The analysis module is also used to obtain the real-time number of people in the project data, obtain the difference between the average number of workers and the real-time number of people, and determine the adjustment coefficient for adjusting the workload score Mi of the project data based on the difference between the difference and the preset difference.

7. The digital asset management platform for engineering projects as described in claim 6, characterized in that, The analysis module is also used to determine the adjustment coefficient for adjusting the workload score Mi of the project data based on the difference between the number of people and a preset difference between the number of people, including: The analysis module is also configured with a first preset number difference and a second preset number difference, and the first preset number difference is less than the second preset number difference. The analysis module is also used to determine the adjustment coefficient for adjusting the workload score Mi of the project data based on the relationship between the difference in the number of people and each preset difference in the number of people: When the difference in the number of people is lower than or equal to the first preset difference in the number of people, the analysis module determines that the adjustment coefficient is N3; When the difference in the number of people is higher than the first preset difference in the number of people, and the difference in the number of people is lower than or equal to zero, the analysis module determines that the adjustment coefficient is N2; When the difference in the number of people is higher than zero, and the difference in the number of people is lower than or equal to the second preset difference in the number of people, the analysis module determines the adjustment coefficient to be N1. When the difference in the number of people is higher than the second preset difference in the number of people, the analysis module determines that the adjustment coefficient is N0; Where 0.5 < N0 < N1 < N2 < N3 < 1.

8. The digital asset management platform for engineering projects as described in claim 7, characterized in that, When the analysis module determines the adjustment coefficient for adjusting the workload score Mi to be Ni, i = 0, 1, 2, 3, including: The analysis module is also used to obtain the average workload of each project's data; The analysis module is also used to obtain the workload difference between the average workload and the total workload of the project data, and to determine whether to correct the adjustment coefficient Ni based on the workload difference. When the difference in workload is less than zero, the analysis module determines that the adjustment coefficient Ni does not need to be corrected. When the workload difference is greater than or equal to zero, the analysis module corrects the adjustment coefficient Ni based on the workload difference and determines the correction parameter of the adjustment coefficient Ni.

9. The digital asset management platform for engineering projects as described in claim 8, characterized in that, When the analysis module corrects the adjustment coefficient Ni based on the workload difference and determines the correction parameter of the adjustment coefficient Ni, it includes: The analysis module is also configured with a first preset workload difference and a second preset workload difference, wherein the first preset workload difference is less than the second preset workload difference. The analysis module is also used to determine the correction parameter of the adjustment coefficient Ni based on the relationship between the workload difference and each preset workload difference; When the workload difference is lower than or equal to the first preset workload difference, the analysis module determines that the correction parameter of the adjustment coefficient Ni is B1. When the workload difference is higher than the first preset workload difference and the workload difference is lower than or equal to the second preset workload difference, the analysis module determines that the correction parameter of the adjustment coefficient Ni is B2. When the workload difference is higher than the second preset workload difference, the analysis module determines that the correction parameter of the adjustment coefficient Ni is B3; Furthermore, B1 < B2 < B3.

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