CN116739225B - Project execution process monitoring method and system - Google Patents
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Abstract
The disclosed technical scheme comprises the steps of carrying out first project disassembly on an executed main project to generate a plurality of parallel sub-projects, carrying out first event disassembly on the parallel sub-projects to obtain a plurality of event factors, generating a process reference strategy according to the characteristics of the event factors, monitoring the project execution process by utilizing the process reference strategy to generate a first project deduction process, determining the credibility of the process reference strategy according to the actual achievement event and deduction achievement time difference of project nodes, and determining whether to select the process reference strategy to monitor the project process according to the credibility, so that the accurate judgment and display of the complex project execution process are realized.
Description
Technical Field
The invention relates to the technical field of project monitoring, in particular to a project execution process monitoring method and system.
Background
In project management, monitoring project execution processes is critical to ensure that projects proceed smoothly as planned and achieve the intended goals. As the complexity and scale of projects continue to grow, efficient project monitoring methods become one of the key factors in project success.
In the prior art, a method for monitoring the execution progress of an item generally determines the completion conditions of nodes of different items, and although such a method can display the execution conditions of the item to a certain extent, the completion degree of the item cannot be quantitatively determined, and particularly the execution conditions of the item cannot be precisely known even more and more complex items are faced, so that a method and a system capable of precisely displaying the execution conditions of the complex items are needed.
Disclosure of Invention
The invention aims to provide a method and a system capable of accurately judging the execution process of a complex project.
The invention discloses a project execution process monitoring method, which comprises the following steps:
performing first project disassembly on the executed main project, and generating a plurality of parallel sub-projects through the main project disassembly;
carrying out first event disassembly on the parallel sub-items to obtain a plurality of event factors, and generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors;
monitoring a project execution process by using a progress reference strategy to generate a first project deduction process, and comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process to determine the credibility of the progress reference strategy;
and determining whether to refer to the project process monitored according to the process reference strategy by taking the credibility as a comparison basis.
In some embodiments of the present application, a method for performing a first project disassembly on an execution subject project includes:
defining a plurality of preset main body items aiming at the monitoring requirement of the work items;
aiming at each preset main body project, a plurality of levels of fixed dismantling nodes are set;
determining the number of times of first project disassembly according to the project characteristics of the executed main project, determining a plurality of fixed disassembly nodes with different levels for each first project disassembly, and determining the activated dynamic disassembly nodes based on the configured execution resources;
and disassembling the execution item according to the fixed disassembly node and the dynamic disassembly node to obtain a plurality of parallel sub-items.
In some embodiments of the present application, a method of determining an enabled dynamic tear down node based on configured execution resources includes:
disassembling the executed main body project according to the determined fixed disassembling node to obtain a plurality of project subsections;
analyzing the configured execution resources, determining project subsections to which different execution resource factors belong, and determining the position of the enabled dynamic disassembly node relative to the executed main project according to the termination condition of the execution object of the execution resources in the project subsections.
In some embodiments of the present application, a method for performing first event disassembly on parallel sub-items to obtain a plurality of event factors includes:
aiming at past projects, executing records, and establishing a project feature-event record library, wherein the project feature-event record library comprises a plurality of single projects, each single project comprises a plurality of project features, each single project is also associated with an event factor set, and the event factor set comprises a plurality of event factors;
analyzing the project characteristic-event record library, determining the project characteristic of the same project monomer as input data, determining the event factor set of the same project monomer as output data for supervised learning, and generating an event factor judging model;
when the first event is disassembled for the parallel sub-items:
and analyzing the project characteristics of the executed parallel sub-projects, determining all project characteristics of the parallel sub-projects, and determining event factor sets corresponding to the parallel sub-projects through the event factor judgment model analysis.
In some embodiments of the present application, a method of determining a characteristic of an item includes:
performing scanning analysis on the past project execution records, and classifying the main body projects of different past executions by taking the same type as a classification standard;
according to a preset item keyword reference table, carrying out semantic scanning analysis on the previously executed main body items belonging to the same category, and determining item keyword sets of different main body items;
analyzing all item keyword sets corresponding to past subject items belonging to the same category, and determining a first frequency of occurrence of each item keyword;
determining a correlation degree corresponding value of each item keyword according to the first frequency of occurrence of each item keyword and the keyword emphasis conversion coefficient configured by the item keyword reference table for each item keyword;
comparing the correlation degree corresponding values of different item keywords with the preset correlation degree corresponding values, screening out a plurality of item keywords belonging to the same category according to comparison results, and constructing the screened item keywords into item feature keyword sets of the same category;
the expression for calculating the correlation degree corresponding value of each item keyword is as follows:
；
wherein,the key word emphasis conversion coefficient for the i-th item key word, < ->The first frequency of the ith item keyword, b is the frequency adjustment constant.
In some embodiments of the present application, a method for generating a process reference policy according to an event processing difficulty of an event factor and an association characteristic between each other includes:
predefining the processing difficulty of the event factors to obtain a first processing difficulty value of the corresponding event factors;
correcting the first processing difficulty value according to feedback of the processing time length of the event factor to obtain a second processing difficulty value;
analyzing the association characteristics among different event factors, setting a plurality of association nodes on the different event factors according to analysis results, and constructing node execution sequences among the different association nodes;
intercepting event factor sections among associated nodes corresponding to the execution order of adjacent nodes, and based on the node execution order, sequencing and combining the event factor sections to generate a process reference strategy.
In some embodiments of the present application, a method of determining a degree of trustworthiness of a process reference policy includes:
analyzing the parallel sub-projects, determining the preset consumption time for achieving each project node, and constructing a credibility conversion coefficient according to the preset consumption time consumed by each project node;
combining the credibility conversion coefficient and the first time difference quantity of the actual achievement time and the deduction achievement time to construct a credibility sub operator of the project node;
and determining the credibility of the process reference strategy according to the credibility sub-operators of the nodes of different projects.
In some embodiments of the present application, the expression for the degree of trustworthiness of the computing process reference policy is:
；
wherein y is the corresponding value of the credibility of the reference strategy of the computing process,for the confidence level conversion coefficient corresponding to the nth item node,>for the preset elapsed time of each project node, c1 is the first time adjustment constant, +.>For the first time difference amount, ++>The constant is adjusted for a second time.
In some embodiments of the present application, there is also disclosed a project execution process monitoring system, including:
the project disassembly module is used for carrying out first project disassembly on the executed main project, and generating a plurality of parallel sub-projects through the main project disassembly;
the event disassembly module is used for carrying out first event disassembly on the parallel sub-items to obtain a plurality of event factors;
the process reference strategy generation module is used for generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors;
the credibility calculation module is used for monitoring the project execution process by utilizing the progress reference strategy, generating a first project deduction process, comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process, determining the credibility of the process reference strategy, and determining whether to display the project process monitored according to the process reference strategy by taking the credibility as a comparison basis.
In some embodiments of the present application, the project execution process monitoring system further includes:
and the display module is used for displaying the whole project process of the main project and the sub-project process of the parallel sub-project.
The disclosed technical scheme comprises the steps of carrying out first project disassembly on an executed main project to generate a plurality of parallel sub-projects, carrying out first event disassembly on the parallel sub-projects to obtain a plurality of event factors, generating a process reference strategy according to the characteristics of the event factors, monitoring an project execution process by utilizing the process reference strategy to generate a first project deduction process, determining the credibility of the process reference strategy according to the actual achievement event and deduction achievement time difference of project nodes, and determining whether to select the process reference strategy to monitor the project process according to the credibility.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
Fig. 1 is a method step diagram of a project execution progress monitoring method in an embodiment of the present application.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments, it being understood that the preferred embodiments described herein are for illustrating and explaining the present invention only and are not to be construed as limiting the scope of the present invention, and that some insubstantial modifications and adaptations can be made by those skilled in the art in light of the following disclosure. In the present invention, unless explicitly specified and defined otherwise, technical terms used in the present application should be construed in a general sense as understood by those skilled in the art to which the present invention pertains. The terms "connected," "fixedly," "disposed" and the like are to be construed broadly and may be fixedly connected, detachably connected or integrally formed; can be directly connected or indirectly connected through an intermediate medium; either mechanically or electrically. Unless explicitly defined otherwise.
Examples:
the invention aims to provide a method and a system capable of accurately judging the execution process of a complex project.
The invention discloses a project execution process monitoring method, referring to fig. 1, comprising the following steps:
step S100, performing first project disassembly on the executed subject project, and generating a plurality of parallel sub-projects through the subject project disassembly.
It is to be understood that a main item comprises a plurality of parallel sub-items, and the parallel sub-items do not interfere or interfere with each other or are not obvious, so that the sub-module monitoring of the main item is realized, and the overall process of the main item can be clearly shown.
Step S200, carrying out first event disassembly on the parallel sub-items to obtain a plurality of event factors, and generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors.
It should be understood that a parallel sub-item includes a plurality of event factors, there may be a front-to-back relation of execution among the event factors, and processing difficulties of different event factors are different, and execution resources and time to be consumed for processing are also different, so that processing difficulties and execution association relations among each other are configured for different event factors, and further a process reference policy is constructed and generated, and after the processing difficulties and the execution association relations among each other are configured, a rate of executing a process is determined according to the configured execution resources.
Step S300, monitoring the project execution process by using the progress reference strategy, generating a first project deduction process, and comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process to determine the credibility of the progress reference strategy.
And step S400, determining whether to refer to the project process monitored according to the process reference strategy by taking the credibility as a comparison basis.
In some embodiments of the present application, a method for performing a first project disassembly on an execution subject project includes:
first, a plurality of preset subject items are defined according to work item monitoring requirements.
Second, for each preset subject item, a plurality of level fixed disassembly nodes are set.
And thirdly, determining the number of times of first project disassembly according to project characteristics of executed subject projects, determining a plurality of fixed disassembly nodes with different levels for each first project disassembly, and determining the started dynamic disassembly nodes based on configured execution resources.
It should be understood that the project characteristics of the subject project include the category of the project, the task that the project needs to complete, the project completion index and the project execution resource, and in the case where some project characteristics of the subject project are matched, there is a common fixed disassembly node, for example, the subject project a, its project characteristic group a (A1, A2, A3), in the case where the subject project a satisfies the project characteristic group a (A1, A2, A3), it is necessary to include the A1 sub-project, the A2 sub-project, the A3 sub-project, and there is a characteristic of low mutual constraint between the sub-projects, in which case, if other subject projects satisfy the characteristic group a (A1, A2, A3), it means that the disassembly node may also be set according to the above rule, and in the case where the sub-projects that have been disassembled may be further disassembled, further, the sub-projects may be further disassembled, resulting in smaller sub-projects.
It is to be understood that the process of a subject item can be determined and judged more clearly by disassembling the subject item multiple times.
And fourthly, disassembling the execution project according to the fixed disassembly node and the movable disassembly node to obtain a plurality of parallel sub-projects.
In some embodiments of the present application, a method of determining an enabled dynamic tear down node based on configured execution resources includes:
and the first step, after the executed main body project is disassembled according to the determined fixed disassembly node, a plurality of project subsections are obtained.
And secondly, analyzing the configured execution resources, determining project subsections to which different execution resource factors belong, and determining the position of the enabled dynamic disassembly node relative to the executed main project according to the termination condition of the execution resource on the execution object of the project subsections.
It should be understood that the execution resource factor may be a human, a device in a broad sense, or other resource consumed for executing the project, and the termination condition of the execution resource includes execution of the project subsection, consumption of the execution resource, or meeting a need to change the execution resource.
In some embodiments of the present application, a method for performing first event disassembly on parallel sub-items to obtain a plurality of event factors includes:
the method comprises the steps of firstly, recording is carried out for past projects, and a project feature-event record library is established, wherein the project feature-event record library comprises a plurality of single projects, each single project comprises a plurality of project features, each single project is also associated with an event factor set, and the event factor set comprises a plurality of event factors.
And secondly, analyzing the project characteristic-event record library, determining the project characteristic of the same project monomer as input data, determining the event factor set of the same project monomer as output data, performing supervised learning, and generating an event factor judging model.
When the first event is disassembled for the parallel sub-items:
and analyzing the project characteristics of the executed parallel sub-projects, determining all project characteristics of the parallel sub-projects, and determining event factor sets corresponding to the parallel sub-projects through the event factor judgment model analysis.
It should be appreciated that supervised learning is a machine learning method by which models are trained using labeled training data to predict labels for unknown data or to classify. The following is a general supervised learning step:
and (3) data collection: training data with labels (known results) are collected. These data should include the input features and corresponding labels (output results).
Data preprocessing: preprocessing the collected training data, including data cleaning, missing value processing, outlier detection, feature scaling and the like, to ensure the quality and consistency of the data.
Characteristic engineering: suitable features are selected and extracted according to the specific requirements of the task and the features of the data. This may involve methods of feature selection, feature transformation, feature construction, and the like.
Model selection: an appropriate supervised learning model is selected. Depending on the nature of the problem and the nature of the data. Common supervised learning models include decision trees, logistic regression, support vector machines, neural networks, and the like.
Model training: the selected model is trained using the preprocessed training data. The model adjusts its own parameters by learning the relationship between the input features and the corresponding labels.
Model evaluation: the trained model is evaluated using a test dataset that is independent of the training data. Common evaluation indexes include accuracy, precision, recall, F1 score, and the like.
Model optimization: and optimizing the model according to the evaluation result. This may include adjusting model parameters, trying different feature selection methods, using regularization, etc.
Prediction and application: and predicting or classifying the unknown data by using the optimized model. The model will output a corresponding label or prediction based on the input features.
It should be noted that the above steps are an iterative process. In practice, multiple adjustments and optimization of the model may be required, with the use of cross-validation techniques for model selection and evaluation to ensure that a final model with good generalization capability is obtained.
In some embodiments of the present application, a method of determining a characteristic of an item includes:
first, the record of past items is scanned and analyzed, and the subject items of different past executions are classified by taking the same type as a classification standard.
Secondly, carrying out semantic scanning analysis on the previously executed subject items of the same category according to a preset item keyword reference table, and determining item keyword sets of different subject items.
And thirdly, analyzing all item keyword sets corresponding to the past subject items belonging to the same category, and determining the first frequency of occurrence of each item keyword.
And fourthly, determining a corresponding value of the degree of correlation of each item keyword according to the first frequency of occurrence of each item keyword and the keyword emphasis conversion coefficient configured for each item keyword by the item keyword reference table.
And fifthly, comparing the correlation degree corresponding values of the different item keywords with the preset correlation degree corresponding values, screening out a plurality of item keywords belonging to the same category according to comparison results, and constructing the screened item keywords into item feature keyword sets of the same category.
The expression for calculating the correlation degree corresponding value of each item keyword is as follows:
。
wherein,the key word emphasis conversion coefficient for the i-th item key word, < ->The first frequency of the ith item keyword, b is the frequency adjustment constant.
In some embodiments of the present application, a method for generating a process reference policy according to an event processing difficulty of an event factor and an association characteristic between each other includes:
the first step, predefining the processing difficulty of the event factors to obtain a first processing difficulty value of the corresponding event factors.
And secondly, correcting the first processing difficulty value according to feedback of the processing time of the event factor to obtain a second processing difficulty value.
It should be understood that the feedback of the processing duration of the event factor mentioned here may be a field feedback during execution.
And thirdly, analyzing the association characteristics among different event factors, setting a plurality of association nodes on the different event factors according to analysis results, and constructing node execution sequences among the different association nodes.
And fourthly, intercepting event factor sections among associated nodes corresponding to the execution sequence of the adjacent nodes, and sequencing and combining the event factor sections based on the execution sequence of the nodes to generate a process reference strategy.
In some embodiments of the present application, a method of determining a degree of trustworthiness of a process reference policy includes:
the first step, analyzing the parallel sub-projects, determining the preset consumption time for achieving each project node, and constructing a credibility conversion coefficient according to the preset consumption time consumed by each project node.
And secondly, combining the credibility conversion coefficient with the first time difference of the actual achievement time and the deduction achievement time to construct a credibility sub operator of the project node.
And thirdly, determining the credibility of the process reference strategy according to the credibility sub-operators of the nodes of different projects.
In some embodiments of the present application, the expression for the degree of trustworthiness of the computing process reference policy is:
；
wherein y is the corresponding value of the credibility of the reference strategy of the computing process,for the confidence level conversion coefficient corresponding to the nth item node,>for the preset elapsed time of each project node, c1 is the first time adjustment constant, +.>For the first time difference amount, ++>The constant is adjusted for a second time.
In some embodiments of the present application, there is also disclosed a project execution process monitoring system, including: the system comprises a project disassembly module, an event disassembly module, a process reference strategy generation module and a credibility calculation module.
The project disassembly module is used for carrying out first project disassembly on the executed main project, and a plurality of parallel sub-projects are generated through the main project disassembly.
The event disassembling module is used for performing first event disassembling on the parallel sub-items to obtain a plurality of event factors.
The process reference strategy generation module is used for generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors.
The credibility calculation module is used for monitoring the project execution process by utilizing the progress reference strategy, generating a first project deduction process, comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process, determining the credibility of the process reference strategy, and determining whether to display the project process monitored according to the process reference strategy by taking the credibility as a comparison basis.
In some embodiments of the present application, the project execution process monitoring system further includes: and the display module is used for displaying the whole project process of the main project and the sub-project process of the parallel sub-project.
The disclosed technical scheme comprises the steps of carrying out first project disassembly on an executed main project to generate a plurality of parallel sub-projects, carrying out first event disassembly on the parallel sub-projects to obtain a plurality of event factors, generating a process reference strategy according to the characteristics of the event factors, monitoring an project execution process by utilizing the process reference strategy to generate a first project deduction process, determining the credibility of the process reference strategy according to the actual achievement event and deduction achievement time difference of project nodes, and determining whether to select the process reference strategy to monitor the project process according to the credibility.
From the above description of the embodiments, it will be clear to those skilled in the art that the present invention may be implemented in hardware, or may be implemented by means of software plus necessary general hardware platforms. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective implementation scenario of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (3)
1. A method for monitoring an execution progress of an item, comprising:
performing first project disassembly on the executed main project, and generating a plurality of parallel sub-projects through the main project disassembly;
carrying out first event disassembly on the parallel sub-items to obtain a plurality of event factors, and generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors;
monitoring a project execution process by using a progress reference strategy to generate a first project deduction process, and comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process to determine the credibility of the progress reference strategy;
determining whether to refer to monitoring the project process according to a process reference strategy by taking the credibility as a judgment basis;
the method for carrying out first project disassembly on the execution subject project comprises the following steps:
defining a plurality of preset main body items aiming at the monitoring requirement of the work items;
aiming at each preset main body project, a plurality of levels of fixed dismantling nodes are set;
determining the number of times of first project disassembly according to the project characteristics of the executed main project, determining a plurality of fixed disassembly nodes with different levels for each first project disassembly, and determining the activated dynamic disassembly nodes based on the configured execution resources;
disassembling the execution item according to the fixed disassembly node and the movable disassembly node to obtain a plurality of parallel sub-items;
the method for determining the enabled dynamic disassembly node based on the configured execution resources comprises the following steps:
disassembling the executed main body project according to the determined fixed disassembling node to obtain a plurality of project subsections;
analyzing the configured execution resources, determining project subsections to which different execution resource factors belong, and determining the position of an enabled dynamic disassembly node relative to an executed main project according to the termination condition of the execution object of the execution resources in the project subsections;
the method for disassembling the first event to the parallel sub-items to obtain a plurality of event factors comprises the following steps:
aiming at past projects, executing records, and establishing a project feature-event record library, wherein the project feature-event record library comprises a plurality of single projects, each single project comprises a plurality of project features, each single project is also associated with an event factor set, and the event factor set comprises a plurality of event factors;
analyzing the project characteristic-event record library, determining the project characteristic of the same project monomer as input data, determining the event factor set of the same project monomer as output data for supervised learning, and generating an event factor judging model;
when the first event is disassembled for the parallel sub-items:
analyzing the item characteristics of the executed parallel sub-items, determining all item characteristics of the parallel sub-items, and determining event factor sets corresponding to the parallel sub-items through analysis of the event factor judgment model;
the method for determining the project characteristics comprises the following steps:
performing scanning analysis on the past project execution records, and classifying the main body projects of different past executions by taking the same type as a classification standard;
according to a preset item keyword reference table, carrying out semantic scanning analysis on the previously executed main body items belonging to the same category, and determining item keyword sets of different main body items;
analyzing all item keyword sets corresponding to past subject items belonging to the same category, and determining a first frequency of occurrence of each item keyword;
determining a correlation degree corresponding value of each item keyword according to the first frequency of occurrence of each item keyword and the keyword emphasis conversion coefficient configured by the item keyword reference table for each item keyword;
comparing the correlation degree corresponding values of different item keywords with the preset correlation degree corresponding values, screening out a plurality of item keywords belonging to the same category according to comparison results, and constructing the screened item keywords into item feature keyword sets of the same category;
the expression for calculating the correlation degree corresponding value of each item keyword is as follows:
；
wherein,the key word emphasis conversion coefficient for the i-th item key word, < ->The first frequency of the ith item keyword, b is a frequency adjustment constant;
the method for generating the process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics of the event factors comprises the following steps:
predefining the processing difficulty of the event factors to obtain a first processing difficulty value of the corresponding event factors;
correcting the first processing difficulty value according to feedback of the processing time length of the event factor to obtain a second processing difficulty value;
analyzing the association characteristics among different event factors, setting a plurality of association nodes on the different event factors according to analysis results, and constructing node execution sequences among the different association nodes;
intercepting event factor sections among associated nodes corresponding to the execution sequence of adjacent nodes, and sequencing and combining the event factor sections based on the node execution sequence to generate a process reference strategy;
the method for determining the credibility of the process reference strategy comprises the following steps:
analyzing the parallel sub-projects, determining the preset consumption time for achieving each project node, and constructing a credibility conversion coefficient according to the preset consumption time consumed by each project node;
combining the credibility conversion coefficient and the first time difference quantity of the actual achievement time and the deduction achievement time to construct a credibility sub operator of the project node;
determining the credibility of a process reference strategy according to credibility sub operators of different project nodes;
the expression of the degree of trust of the computing process reference policy is:
；
wherein y is the corresponding value of the credibility of the reference strategy of the computing process,for the confidence level conversion coefficient corresponding to the nth item node,>for the preset elapsed time of each project node, c1 is the first time adjustment constant, +.>For the first time difference amount, ++>The constant is adjusted for a second time.
2. An item execution process monitoring system, comprising:
the project disassembly module is used for carrying out first project disassembly on the executed main project, and generating a plurality of parallel sub-projects through the main project disassembly;
the event disassembly module is used for carrying out first event disassembly on the parallel sub-items to obtain a plurality of event factors;
the process reference strategy generation module is used for generating a process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics among the event factors;
the credibility calculation module is used for monitoring the project execution process by utilizing the progress reference strategy, generating a first project deduction process, comparing and analyzing the actual achievement time of the project node in the project execution process with the deduction achievement time of the corresponding project node in the first project deduction process, determining the credibility of the process reference strategy, and determining whether to display the project process monitored according to the process reference strategy by taking the credibility as a comparison basis;
the project execution process monitoring system determines whether to monitor the project process according to a process reference strategy by taking the credibility as a judgment basis;
the method for carrying out first project disassembly on the execution subject project by the event disassembly module comprises the following steps:
defining a plurality of preset main body items aiming at the monitoring requirement of the work items;
aiming at each preset main body project, a plurality of levels of fixed dismantling nodes are set;
determining the number of times of first project disassembly according to the project characteristics of the executed main project, determining a plurality of fixed disassembly nodes with different levels for each first project disassembly, and determining the activated dynamic disassembly nodes based on the configured execution resources;
disassembling the execution item according to the fixed disassembly node and the movable disassembly node to obtain a plurality of parallel sub-items;
the method for determining the enabled dynamic disassembly node based on the configured execution resources comprises the following steps:
disassembling the executed main body project according to the determined fixed disassembling node to obtain a plurality of project subsections;
analyzing the configured execution resources, determining project subsections to which different execution resource factors belong, and determining the position of an enabled dynamic disassembly node relative to an executed main project according to the termination condition of the execution object of the execution resources in the project subsections;
the method for disassembling the first event to the parallel sub-items to obtain a plurality of event factors comprises the following steps:
aiming at past projects, executing records, and establishing a project feature-event record library, wherein the project feature-event record library comprises a plurality of single projects, each single project comprises a plurality of project features, each single project is also associated with an event factor set, and the event factor set comprises a plurality of event factors;
analyzing the project characteristic-event record library, determining the project characteristic of the same project monomer as input data, determining the event factor set of the same project monomer as output data for supervised learning, and generating an event factor judging model;
when the first event is disassembled for the parallel sub-items:
analyzing the item characteristics of the executed parallel sub-items, determining all item characteristics of the parallel sub-items, and determining event factor sets corresponding to the parallel sub-items through analysis of the event factor judgment model;
the method for determining the project characteristics comprises the following steps:
performing scanning analysis on the past project execution records, and classifying the main body projects of different past executions by taking the same type as a classification standard;
according to a preset item keyword reference table, carrying out semantic scanning analysis on the previously executed main body items belonging to the same category, and determining item keyword sets of different main body items;
analyzing all item keyword sets corresponding to past subject items belonging to the same category, and determining a first frequency of occurrence of each item keyword;
determining a correlation degree corresponding value of each item keyword according to the first frequency of occurrence of each item keyword and the keyword emphasis conversion coefficient configured by the item keyword reference table for each item keyword;
comparing the correlation degree corresponding values of different item keywords with the preset correlation degree corresponding values, screening out a plurality of item keywords belonging to the same category according to comparison results, and constructing the screened item keywords into item feature keyword sets of the same category;
the expression for calculating the correlation degree corresponding value of each item keyword is as follows:
；
wherein,the key word emphasis conversion coefficient for the i-th item key word, < ->The first frequency of the ith item keyword, b is a frequency adjustment constant;
the method for generating the process reference strategy according to the event processing difficulty of the event factors and the correlation characteristics of the event factors comprises the following steps:
predefining the processing difficulty of the event factors to obtain a first processing difficulty value of the corresponding event factors;
correcting the first processing difficulty value according to feedback of the processing time length of the event factor to obtain a second processing difficulty value;
analyzing the association characteristics among different event factors, setting a plurality of association nodes on the different event factors according to analysis results, and constructing node execution sequences among the different association nodes;
intercepting event factor sections among associated nodes corresponding to the execution sequence of adjacent nodes, and sequencing and combining the event factor sections based on the node execution sequence to generate a process reference strategy;
the method for determining the credibility of the process reference strategy comprises the following steps:
analyzing the parallel sub-projects, determining the preset consumption time for achieving each project node, and constructing a credibility conversion coefficient according to the preset consumption time consumed by each project node;
combining the credibility conversion coefficient and the first time difference quantity of the actual achievement time and the deduction achievement time to construct a credibility sub operator of the project node;
determining the credibility of a process reference strategy according to credibility sub operators of different project nodes;
the expression of the degree of trust of the computing process reference policy is:
；
wherein y is the corresponding value of the credibility of the reference strategy of the computing process,for the confidence level conversion coefficient corresponding to the nth item node,>for the preset elapsed time of each project node, c1 is the first time adjustment constant, +.>For the first time difference amount, ++>The constant is adjusted for a second time.
3. The project execution progress monitoring system of claim 2, further comprising:
and the display module is used for displaying the whole project process of the main project and the sub-project process of the parallel sub-project.
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