CN116187932A - Information system engineering supervision project risk self-adaptive assessment method - Google Patents
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Abstract
The invention discloses a risk self-adaptive evaluation method for information system engineering supervision projects, which comprises the following steps: step 1, evaluating a model; step 2, evaluating a system; step 3, data acquisition; step 4, evaluating and analyzing; and 5, risk optimization. According to the invention, based on BP neural network technology, an information system engineering supervision project risk self-adaptive assessment model is established, project risk is comprehensively, truly and accurately reflected through the assessment model, based on blockchain technology and by combining the assessment model, an information system engineering supervision project risk self-adaptive assessment system is established, risk level assessment is carried out on the information system engineering supervision project, project assessment risk factors are analyzed, an initial global risk view and report of the project risk assessment are generated, the project risk supervision assessment is more accurate, the project safety is improved, the assessment model is self-adaptively updated by combining current project data, the assessment model has strong adaptability, and the project supervision is reliable.
Description
Technical Field
The invention relates to the technical field of project assessment, in particular to a risk self-adaptive assessment method for information system engineering supervision projects.
Background
The project risk assessment is to ensure the final smooth implementation of the project, comprehensively analyze the influence of project risk factors, shape a risk system evaluation model, analyze the probability of occurrence of several types of risks and the possible loss situation caused by the risks by the system, further obtain important risk factors of the project, and provide a scientific and reasonable method for effectively treating the important risk factors. Project risk assessment is closely related to effectiveness and scientificity of project risk management, and has important influence on risk handling. The evaluation method can be generally divided into three types of qualitative, quantitative, qualitative and quantitative combination, and the effective project risk evaluation method generally adopts a system method combining qualitative and quantitative combination. The risk evaluation method for the project mainly comprises a decision tree method, a risk map evaluation method, a fuzzy risk comprehensive evaluation method, a Monte Carlo simulation method, a hierarchical analysis method and the like.
At present, project risk assessment is carried out by a supervision person according to project human factors, project equipment factors and project environment factors, the influence on the human factors existing in the project risk assessment is large, the risk assessment is easy to be subjected to subjective interference of supervision, and the project risk assessment is one-sided and has poor accuracy.
Disclosure of Invention
The invention aims to provide an information system engineering supervision project risk self-adaptive assessment method so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the self-adaptive risk assessment method for the information system engineering supervision project comprises the following steps:
step 1, evaluating a model: based on BP neural network technology, establishing an information system engineering supervision project risk self-adaptive evaluation model, and comprehensively, truly and accurately reflecting the project risk through the evaluation model;
step 2, an evaluation system: based on a blockchain technology, combining an evaluation model, and establishing an information system engineering supervision project risk self-adaptive evaluation system;
step 3, data acquisition: acquiring factor data for risk assessment of an information system engineering supervision project;
step 4, evaluation analysis: inputting the data into an evaluation system, performing risk level evaluation on an information system engineering supervision project through an evaluation model in the system, analyzing project evaluation risk factors, and generating an initial global risk view and report of project risk evaluation;
step 5, risk optimization: and optimizing the information system engineering supervision project based on the risk level and the risk factor of the information system engineering supervision project evaluation to obtain an evaluation optimization scheme.
Preferably, the information system engineering supervision project data collection comprises project artificial factors, project equipment factors and project environment factors.
Preferably, the method for establishing the evaluation model comprises the following steps:
s1, a database: collecting and arranging historical data of information system engineering supervision projects, establishing a database of the information system engineering supervision projects, and recording characteristic items contained in each collected sample data;
s2, testing: determining a training set and a testing set, and selecting information system engineering supervision project attributes and defining neurons;
s3, data processing: preprocessing the missing data, expressing the null value by NaN, and processing the data containing NaN before training the network in order to construct an evaluation model of the deep BP neural network with accuracy;
s4, data training: establishing an evaluation model of a deep BP neural network based on risk training set data of an information system engineering supervision project, and training the network by using the training set data;
s5, analysis: the test set data is input into an evaluation model, and the sensitivity and the specificity of the evaluation model are analyzed.
Preferably, the self-adaptive evaluation system comprises an acquisition module, a processing module, a main control module, an evaluation model unit, a storage module, a self-adaptive module, a prediction module and a remote management unit, wherein the output end of the acquisition module is electrically connected with the input end of the main control module through the processing module, and the output end of the main control module is electrically connected with the evaluation model unit, the storage module, the self-adaptive module, the prediction module and the remote management unit;
the evaluation model unit evaluates the project risk based on project supervision data acquired by the acquisition module to obtain a project risk evaluation report; the self-adaptive module is used for carrying out self-adaptive updating on the evaluation model by combining the current project data by applying the project data to the historical data of the evaluation model after the project is ended based on the project data stored by the storage module; and the prediction module is used for analyzing and predicting the risk of the project based on the project supervision data acquired by the acquisition module.
Preferably, the prediction module comprises a software prediction and an experience prediction, the software prediction predicts the risk of the supervision project through project prediction software, the experience prediction is based on judgment prediction made by experienced project supervision personnel on the project, the prediction module synthesizes the software prediction and the experience prediction to obtain a comprehensive prediction result, and the prediction result of the prediction module is compared with a project risk assessment report of the assessment model unit.
Preferably, the adaptive evaluation system comprises a data layer, a network layer, a consensus layer, an incentive layer, a contract layer and an application layer;
the data layer is used for establishing a database of a bottommost distributed data structure of the information system engineering supervision project risk self-adaptive evaluation system; the network layer is used for node distribution storage of the information system engineering supervision project risk self-adaptive evaluation system; the consensus layer is used for providing a consensus algorithm for the risk self-adaptive evaluation system of the information system engineering supervision project; the excitation layer is used for an excitation mechanism and an allocation system of the information system engineering supervision project risk self-adaptive evaluation system; the contract layer is used for establishing a code transaction contract of the information system engineering supervision project risk self-adaptive evaluation system; the application layer provides application for the operation of the information system engineering supervision project risk self-adaptive evaluation system.
Preferably, the self-adaptive evaluation system further comprises a login module, wherein the login module is used for a project manager to log in the system and obtain authority for managing project risk evaluation.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the information system engineering supervision project risk self-adaptive assessment method, an information system engineering supervision project risk self-adaptive assessment model is established based on BP neural network technology, project risk is comprehensively, truly and accurately reflected through the assessment model, an information system engineering supervision project risk self-adaptive assessment system is established based on blockchain technology and combined with the assessment model, risk level assessment is conducted on the information system engineering supervision project, project assessment risk factors are analyzed, an initial global risk view and report of project risk assessment are generated, project risk supervision assessment is more accurate, and project safety is improved.
2. According to the information system engineering supervision project risk self-adaptive evaluation method, the self-adaptive module is based on project data stored by the storage module, after the project is finished, the project data is applied to historical data of an evaluation model, the evaluation model is adaptively updated by combining current project data, so that the evaluation model has stronger adaptability, the prediction module is based on the project supervision data collected by the collection module, the risk of the project is analyzed and predicted, the project prediction and the project risk evaluation are comprehensively compared and analyzed, and the reliability of project supervision is improved.
Drawings
FIG. 1 is a flow chart of an adaptive evaluation method according to the present invention;
FIG. 2 is a schematic diagram of the working principle of the adaptive evaluation system according to the present invention;
FIG. 3 is a block chain architecture diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific direction, be configured and operated in the specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "provided," "connected," and the like are to be construed broadly, and may be fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Examples
As shown in fig. 1 to 3, the risk adaptive evaluation method for the information system engineering supervision project of the present embodiment includes the following steps:
step 1, evaluating a model: based on BP neural network technology, establishing an information system engineering supervision project risk self-adaptive evaluation model, and comprehensively, truly and accurately reflecting the project risk through the evaluation model;
step 2, an evaluation system: based on a blockchain technology, combining an evaluation model, and establishing an information system engineering supervision project risk self-adaptive evaluation system;
step 3, data acquisition: acquiring factor data for risk assessment of an information system engineering supervision project;
step 4, evaluation analysis: inputting the data into an evaluation system, performing risk level evaluation on an information system engineering supervision project through an evaluation model in the system, analyzing project evaluation risk factors, and generating an initial global risk view and report of project risk evaluation;
step 5, risk optimization: and optimizing the information system engineering supervision project based on the risk level and the risk factor of the information system engineering supervision project evaluation to obtain an evaluation optimization scheme.
Specifically, the information system engineering supervision project data collection comprises project artificial factors, project equipment factors and project environment factors.
Further, the method for establishing the evaluation model comprises the following steps:
s1, a database: collecting and arranging historical data of information system engineering supervision projects, establishing a database of the information system engineering supervision projects, and recording characteristic items contained in each collected sample data;
s2, testing: determining a training set and a testing set, and selecting information system engineering supervision project attributes and defining neurons;
s3, data processing: preprocessing the missing data, expressing the null value by NaN, and processing the data containing NaN before training the network in order to construct an evaluation model of the deep BP neural network with accuracy;
s4, data training: establishing an evaluation model of a deep BP neural network based on risk training set data of an information system engineering supervision project, and training the network by using the training set data;
s5, analysis: the test set data is input into an evaluation model, and the sensitivity and the specificity of the evaluation model are analyzed.
Further, the self-adaptive evaluation system comprises an acquisition module, a processing module, a main control module, an evaluation model unit, a storage module, a self-adaptive module, a prediction module and a remote management unit, wherein the output end of the acquisition module is electrically connected with the input end of the main control module through the processing module, and the output end of the main control module is electrically connected with the evaluation model unit, the storage module, the self-adaptive module, the prediction module and the remote management unit;
the evaluation model unit evaluates the project risk based on project supervision data acquired by the acquisition module to obtain a project risk evaluation report; the self-adaptive module is used for carrying out self-adaptive updating on the evaluation model by combining the current project data by applying the project data to the historical data of the evaluation model after the project is ended based on the project data stored by the storage module; the prediction module is used for analyzing and predicting the risk of the project based on project supervision data acquired by the acquisition module, and the remote management unit can remotely supervise the project risk assessment.
Further, the prediction module comprises software prediction and experience prediction, the software prediction predicts the project risk of supervision through project prediction software, the experience prediction is based on judgment prediction made by project supervision personnel with abundant experience, the prediction module synthesizes the software prediction and experience prediction to obtain a comprehensive prediction result, the prediction result of the prediction module is compared with a project risk assessment report of the assessment model unit, and when the comparison difference value of the prediction result and the project risk assessment report exceeds the threshold value of project comparison, the project risk assessment report has an error; and when the comparison difference value of the prediction result and the project risk assessment report is within the threshold value range of project comparison, the project risk assessment report is accurate.
Further, the adaptive evaluation system comprises a data layer, a network layer, a consensus layer, an incentive layer, a contract layer and an application layer; the data layer is used for establishing a database of a bottommost distributed data structure of the information system engineering supervision project risk self-adaptive evaluation system; the network layer is used for node distribution storage of the information system engineering supervision project risk self-adaptive evaluation system; the consensus layer is used for providing a consensus algorithm for the risk self-adaptive evaluation system of the information system engineering supervision project; the excitation layer is used for an excitation mechanism and an allocation system of the information system engineering supervision project risk self-adaptive evaluation system; the contract layer is used for establishing a code transaction contract of the information system engineering supervision project risk self-adaptive evaluation system; the application layer provides application for the operation of the information system engineering supervision project risk self-adaptive evaluation system.
Furthermore, the self-adaptive evaluation system also comprises a login module, wherein the login module is used for a project manager to log in the system and obtain the authority of managing the project risk evaluation.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The risk self-adaptive evaluation method for the information system engineering supervision project is characterized by comprising the following steps of: the adaptive evaluation method comprises the following steps:
step 1, evaluating a model: based on BP neural network technology, establishing an information system engineering supervision project risk self-adaptive evaluation model, and comprehensively, truly and accurately reflecting the project risk through the evaluation model;
step 2, an evaluation system: based on a blockchain technology, combining an evaluation model, and establishing an information system engineering supervision project risk self-adaptive evaluation system;
step 3, data acquisition: acquiring factor data for risk assessment of an information system engineering supervision project;
step 4, evaluation analysis: inputting the data into an evaluation system, performing risk level evaluation on an information system engineering supervision project through an evaluation model in the system, analyzing project evaluation risk factors, and generating an initial global risk view and report of project risk evaluation;
step 5, risk optimization: and optimizing the information system engineering supervision project based on the risk level and the risk factor of the information system engineering supervision project evaluation to obtain an evaluation optimization scheme.
2. The information system engineering supervision project risk adaptive assessment method according to claim 1, wherein: the information system engineering supervision project data collection comprises project artificial factors, project equipment factors and project environment factors.
3. The information system engineering supervision project risk adaptive assessment method according to claim 1, wherein: the method for establishing the evaluation model comprises the following steps:
s1, a database: collecting and arranging historical data of information system engineering supervision projects, establishing a database of the information system engineering supervision projects, and recording characteristic items contained in each collected sample data;
s2, testing: determining a training set and a testing set, and selecting information system engineering supervision project attributes and defining neurons;
s3, data processing: preprocessing the missing data, expressing the null value by NaN, and processing the data containing NaN before training the network in order to construct an evaluation model of the deep BP neural network with accuracy;
s4, data training: establishing an evaluation model of a deep BP neural network based on risk training set data of an information system engineering supervision project, and training the network by using the training set data;
s5, analysis: the test set data is input into an evaluation model, and the sensitivity and the specificity of the evaluation model are analyzed.
4. The information system engineering supervision project risk adaptive assessment method according to claim 1, wherein: the self-adaptive evaluation system comprises an acquisition module, a processing module, a main control module, an evaluation model unit, a storage module, a self-adaptive module, a prediction module and a remote management unit, wherein the output end of the acquisition module is electrically connected with the input end of the main control module through the processing module, and the output end of the main control module is electrically connected with the evaluation model unit, the storage module, the self-adaptive module, the prediction module and the remote management unit;
the evaluation model unit evaluates the project risk based on project supervision data acquired by the acquisition module to obtain a project risk evaluation report; the self-adaptive module is used for carrying out self-adaptive updating on the evaluation model by combining the current project data by applying the project data to the historical data of the evaluation model after the project is ended based on the project data stored by the storage module; and the prediction module is used for analyzing and predicting the risk of the project based on the project supervision data acquired by the acquisition module.
5. The information system engineering supervision project risk adaptive assessment method according to claim 4, wherein: the prediction module comprises software prediction and experience prediction, the software prediction predicts the project risk of the supervision project through project prediction software, the experience prediction is based on judgment prediction of project supervision personnel with abundant experience, the prediction module synthesizes the software prediction and experience prediction, and a comprehensive prediction result is obtained, and the prediction result of the prediction module is compared with a project risk assessment report of the assessment model unit.
6. The information system engineering supervision project risk adaptive assessment method according to claim 1, wherein: the self-adaptive evaluation system comprises a data layer, a network layer, a consensus layer, an excitation layer, a contract layer and an application layer;
the data layer is used for establishing a database of a bottommost distributed data structure of the information system engineering supervision project risk self-adaptive evaluation system; the network layer is used for node distribution storage of the information system engineering supervision project risk self-adaptive evaluation system; the consensus layer is used for providing a consensus algorithm for the risk self-adaptive evaluation system of the information system engineering supervision project; the excitation layer is used for an excitation mechanism and an allocation system of the information system engineering supervision project risk self-adaptive evaluation system; the contract layer is used for establishing a code transaction contract of the information system engineering supervision project risk self-adaptive evaluation system; the application layer provides application for the operation of the information system engineering supervision project risk self-adaptive evaluation system.
7. The information system engineering supervision project risk adaptive assessment method according to claim 1, wherein: the self-adaptive evaluation system further comprises a login module, wherein the login module is used for a project supervision personnel to log in the system and obtain authority for supervision project risk evaluation.
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| CN116503026B (en) * | 2023-06-26 | 2024-02-09 | 广东省科技基础条件平台中心 | Operation and maintenance risk assessment method, system and storage medium for science and technology items |
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