CN108241964A - Capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms - Google Patents
Capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms Download PDFInfo
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Abstract
The invention discloses a kind of capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms, including:Risk evaluation results library, construction project information bank, Risk Evaluation Factors configuration, risk evaluation model training, risk evaluation model verification, construction project management and control risk assessment, construction project management and control business processing and mobile terminal displaying control module.Invention is the study and training by neural network model, is utilized a series of evaluation indexes using BP artificial nerve network models（Engineering safety management and control, construction equipment, construction site management and control, security management and control rectification, five forbid check, 5S management, scene punishment deduction of points situation）, construction project engineering risk is evaluated, makes every effort to break away from the influence of artificial subjective factor, makes full use of the knowledge and experience of expert, support is provided for construction project decision.
Description
Technical field
The present invention relates to a kind of capital construction scene management and control mobile solution platforms based on BP artificial nerve network model algorithms.
Background technology
The management and control of capital construction scene is the emphasis of grid company capital construction management work, for the special of Electric Power Capital Construction engineering project
Property, the distribution of project construction field regions is wide, part construction site present position is more remote, there are communication exchanges with management unit
Not in time the problems such as.Therefore, it is necessary to realize the information real-time interaction that both pictures and texts are excellent between grid company and capital construction scene, with
Meet the requirement of capital construction scene management and control work, avoid in the capital construction scene management and control course of work, because scene lacks information-based operation
Platform, cause management and control working stamndard publication not in time, disunity, inaccuracy；Field data collecting and reporting is not in time；Evaluation is inaccurate
The problem of true.
While management and control real-time based on mobile application being carried out to foundation construction site safety risk, problem, the efficient capital construction in backstage
Project management and control Risk Evaluating System is extremely necessary.Artificial neural network system's theory is using the intelligent function of human brain as research
Object and theoretical as the intelligence computation of background using human neuronal cell line information processing method, before being very active in the world at present
Along one of research field.Nineteen forty-three, psychologist W.S.McCulloch and mathematics logician W Pitts establish nerve net
The mathematical model of network, referred to as MP models.They propose the formalization mathematical description and network structure of neuron by MP models
Method, it was demonstrated that single neuron can perform logic function, so as to start the epoch of artificial neural network research.Artificial neuron
Network is a mathematical model to theorize of human brain and its activity, it interconnects structure in the right way by a large amount of processing unit
Into being a large-scale nonlinear adaptive system.Artificial neural network not only simulates biological nervous system in form,
It also has some large-brained essential characteristics.From system form in form, from neuron in itself to linked model, substantially
All it is to be worked in a manner of similar to biological nervous system.From performance characteristic, it makes every effort to simulate biological nervous system
The basic method of operation, artificial neural network system is to handle information to the dynamic response of external input information by its state.
Have in the prior art and risk assessment is carried out to project management using BP neural network, as Yang Jun brightness and Cheng Yinxia exist
It delivers within 2011《Engineering based on BP neural network is small to herd risk assessment》.Presently, there are the problem of be due to engineering project pipe
Control in reason has very big uncertain and ambiguity, the target of project management to have very big uncertainty and ambiguity,
So sample data largely comes from expert analysis mode, and the immediate data from engineering is less.In addition, carry out project performance evaluation
It is also possible to have other data sources such as user investigation with project satisfaction evaluation etc..Due to expert（Or user）What individual judged
Intuition and subjectivity, sample data inevitably have deviation, quality it is difficult to ensure that.If poor, the net of sample data representativeness
Network training is just easily absorbed in Local Minimum or even can not obtain optimal solution.
Invention content
The technical problem to be solved in the present invention：In view of the above-mentioned problems, it is manually refreshing to provide a kind of BP for being capable of self-teaching
Through network model and capital construction scene management and control mobile solution platform.
Technical scheme of the present invention：A kind of capital construction scene management and control mobile application based on BP artificial nerve network model algorithms
Platform, including server and application terminal, server includes:
Risk evaluation results library：Including construction project management and control Risk Evaluation Factors, construction project management and control risk evaluation results and wind
Dangerous evaluation model training result checksum set, for user using BP artificial nerve network models carry out Engineering Risk Assessment, training and
Verification provides support data；
Construction project information bank：Establish the data repository of capital construction scene management and control routine matter processing；
Risk evaluation model is trained：Training sample data are obtained from risk evaluation results library, utilize BP artificial nerve network models
Algorithm trains construction project Engineering Risk Assessment model；
Risk evaluation model verifies：It determines test samples data, examines the result of calculation of risk evaluation model training generation, work as instruction
When practicing sample set overall error less than allowable error, training terminates, and after training successfully, network of relation model weights and threshold value are preserved
To risk evaluation results library；
Construction project management and control risk assessment：The routine matter processing data of typing in construction project information bank are extracted, generate risk
Evaluation index sample data, and risk evaluation model is submitted to calculate generation risk evaluation results, risk evaluation results and evaluation refer to
Standard specimen notebook data is saved in risk evaluation results library.
Risky evaluation index configuration, for the information of project Engineering Risk Assessment index to be configured.
The application terminal is PC terminals and mobile terminal, by PC terminals and mobile terminal by data inputting server,
And invoking server performs the function of risk assessment and information inquiry.
The routine matter processing data of typing in construction project information bank carry out numeralization processing, each in typing list
The input result numerical value of project turns to the number of 0-1, calculates the weighted average of all items on each list, as a wind
Dangerous evaluation index sample data.
Risk evaluation model training obtains training sample data according to the date from risk evaluation results library.
Allocation models Risk Evaluation Factors to be taken, risk evaluation model training root is configured by Risk Evaluation Factors
Training sample data are obtained according to the Risk Evaluation Factors Xiang Congcong risk evaluation results library of configuration.
Beneficial effects of the present invention：
Using BP artificial nerve network models, by the study and training of neural network model, a series of evaluation indexes are utilized
（Engineering safety management and control, construction equipment, construction site management and control, security management and control rectification, five forbid check, 5S management, scene punishment
Deduction of points situation etc.）, construction project engineering risk is evaluated, makes every effort to break away from the influence of artificial subjective factor, makes full use of
The knowledge and experience of expert provides support for construction project decision.
The result calculated every time is preserved, as the sample calculated next time, the quantity of sample database is gradually increased, and keep
Certain validity.In this way with the use of this platform, other than most starting to need some basic samples of typing, it is possible to complete
Self-teaching is carried out by the data accumulation of itself and calculating, the reliability of result of calculation also step up entirely.
By handling the numeralization of administrative situation, reduce and calculate inputted Risk Evaluation Factors item number, and improve
The validity of Risk Evaluation Factors ensures the reliability of result while calculation amount is reduced.
Figure of description：
Fig. 1 is the system construction drawing of the present invention（The system topological figure of the present invention is as shown in Figure 1）.
Fig. 2 is the calculation process of BP neural network model.
Specific embodiment：
Such as figure：Such as Fig. 1, this platform is made of server, PC terminals and mobile terminal of mobile telephone, and server includes：Risk assessment knot
Fruit library 100, construction project information bank 200, Risk Evaluation Factors configuration 300, risk evaluation model training 400, risk assessment mould
Type verification 500, construction project management and control risk assessment 600.Construction project management and control business processing 700 is installed on PC ends, it is mobile whole
There is mobile terminal to show control module 800 on end.
Risk evaluation results library 100：Risk evaluation results library is established, including construction project management and control Risk Evaluation Factors, base
Project management and control risk evaluation results, risk evaluation model training result checksum set are built, BP artificial neural network moulds are used for user
Type carries out Engineering Risk Assessment, training and verification and provides support data；
Construction project information bank 200：The support data of capital construction scene management and control routine matter processing are established, including construction project basis
Information, engineering safety management and control, construction equipment inspection, construction site management and control, security management and control rectification, five forbid check, 5S management
It verifies, the live management and control record such as deduction of points situation is punished at scene.
Risk Evaluation Factors configuration 300：Configuration project Engineering Risk Assessment indication information, establishes construction project engineering risk
Assessment indicator system.
Risk evaluation model training 400：Training sample data, training BP capital construction items are determined from risk evaluation results library 100
Mesh Engineering Risk Assessment neural network model.
Risk evaluation model verification 500：It determines test samples data, examines the number of 400 generation of risk evaluation model training
According to when training sample set overall error is less than allowable error, training terminates.After training successfully, by network of relation model weights and
Threshold value is saved in risk evaluation results library 100.
Construction project management and control risk assessment 600：After management and control information in capital construction scene is submitted to platform service end.By construction project
Management and control risk assessment module 600 generates risk assessment network model sample data, and submits risk assessment network model, by training
Risk assessment network model generation risk evaluation results after success.Risk evaluation results are saved in risk evaluation results library 100,
And it is pushed to mobile terminal displaying control module 800.
Construction project management and control business processing 700：Receive the capital construction scene management and control that mobile terminal displaying control module 800 reports
Information is saved in construction project information bank 200 after processing；The inquiry request of mobile terminal displaying control module 800 is received simultaneously,
After the inquiry of construction project information bank 200, by the dynamic terminal display control module 800 of query result push return.
Mobile terminal shows control module 800：Android device, in the information reporting of capital construction in-situ processing management and control personnel
It is required with information inquiry, and by evaluation result in mobile terminal displaying control module displaying.
The initial calculation of this model is realized by following steps:
（1）Index system is established, collects sample data.According to the target of project management, choose on target it is influential because
Element establishes index system as index.To be comparable each index, target relative degrees are used to qualitative index therein
The method of degree of membership carries out quantification processing, and place is normalized respectively by profit evaluation model, cost type, interval type to quantitative target
Reason.The data of training sample, test samples and analog sample are obtained by approach such as historical summary, expert analysis mode, questionnaire surveys.
In the present embodiment, the index used for：Construction project basic information, engineering safety management and control, construction equipment inspection, construction site
Management and control, security management and control rectification, five forbid to check, 5S management is verified, scene punishment deduction of points situation etc., have number in each record
The data input item that mesh does not wait, numeralization processing after these information form typings, will will be carried out by mobile phone or pc ends, specific to locate
Reason method is that each single item on every table is assigned to the value of 0-1, such as a certain to be qualified, then the numerical value after acquiring is 1, unqualified
It is 0, other situations are 0.5, basically according to linearly being divided to all possibilities, then to all s' on this table
Value is weighted average, obtains the index value of this table, the weighted value of all is all 1 in the present embodiment.
（2）Design BP neural network structure.According to Kolmogorov theorems, for any given ε>0, there are one
Three-layer neural network can approach the nonlinear function of arbitrary any complexity, so being typically chosen three layers of BP with the precision of ε mean square deviations
Neural network.Then, output layer number of nodes and input layer number, root are determined according to project management target and index system
Node in hidden layer is determined according to the quality and quantity of sample and the complexity of project management.
BP（The back propagation learning of Multi-layered Feedforward Networks）Artificial nerve network model is as shown below, and X and Y are networks
Input, output vector.Each neuron is represented with a node.Network is made of input layer, hidden layer and output node layer.It is hidden
Can be one layer or multilayer containing layer, front layer to rear node layer is connected by weighing, i.e., topological structure is directed acyclic graph
Feedforward network.BP artificial nerve network models use BP learning algorithms, and basic thought is least square method, using gradient search
Technology, the error to make the real output value of network and desired output are minimum.
BP learning algorithms are made of forward-propagating and backpropagation.In forward-propagating, input signal is from input layer through hidden
Output layer is transmitted to containing layer.If output layer has obtained desired output, learning algorithm terminates；Otherwise, backpropagation is gone to.Reversely pass
Broadcast is to adjust error signal (sample the exports and difference of network output) each by gradient descent method by former connecting path backwards calculation
The weights and threshold value of layer neuron, reduce error signal.
（3）It trains and examines BP neural network.The training of BP neural network includes information forward-propagating and error reversely passes
Broadcast two processes, by comparing network output valve and the error of desired output, successively change each layer network node weights and
Threshold value, when training sample set overall error E is less than allowable error ε, training terminates.
（4）Input target sample data, calculating target function value.
The target function value that will be obtained（Risk evaluation results）Risk evaluation results library 100 is saved in Risk Evaluation Factors
In, the data in such risk evaluation results library 100 can be more and more, carry out calculating the number just chosen in certain period of time again later
According to input of the sample as model training.
Claims (6)
1. a kind of capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms including server and is answered
With terminal, it is characterised in that server includes:
Risk evaluation results library（100）：Including construction project management and control Risk Evaluation Factors, construction project management and control risk evaluation results
With risk evaluation model training result checksum set, Engineering Risk Assessment, instruction are carried out using BP artificial nerve network models for user
Practice and verification provides support data；
Construction project information bank（200）：Establish the data repository of capital construction scene management and control routine matter processing；
Risk evaluation model is trained（400）：From risk evaluation results library（100）Training sample data are obtained, it is manually refreshing using BP
Construction project Engineering Risk Assessment model is trained through network model algorithm；
Risk evaluation model verifies（500）：It determines test samples data, examines risk evaluation model training（400）The meter of generation
It calculates as a result, when training sample set overall error is less than allowable error, training terminates, and after training successfully, network of relation model is weighed
Value and threshold value are saved in risk evaluation results library（100）；
Construction project management and control risk assessment（600）：Extract construction project information bank（200）The routine matter processing number of middle typing
According to, generation Risk Evaluation Factors sample data, and risk evaluation model is submitted to calculate generation risk evaluation results, risk assessment knot
Fruit and evaluation index sample data are saved in risk evaluation results library（100）.
2. the capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms according to claim 1,
It is characterized in that:Risky evaluation index configuration（300）, for the information of project Engineering Risk Assessment index to be configured.
3. the capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms according to claim 2,
It is characterized in that:The application terminal is PC terminals and mobile terminal, is serviced data inputting by PC terminals and mobile terminal
Device, and invoking server performs the function of risk assessment and information inquiry.
4. any capital construction scene management and control mobile application based on BP artificial nerve network model algorithms according to claim 1-3
Platform, it is characterised in that:Construction project information bank（200）The routine matter processing data of middle typing carry out numeralization processing,
The input result numerical value of each project turns to the number of 0-1 in typing list, and the weighting for calculating all items on each list is put down
Mean value, as a Risk Evaluation Factors sample data.
5. any capital construction scene management and control mobile application based on BP artificial nerve network model algorithms according to claim 1-3
Platform, it is characterised in that:Risk evaluation model is trained（400）According to the date from risk evaluation results library（100）Obtain training sample
Notebook data.
6. the capital construction scene management and control mobile solution platform based on BP artificial nerve network model algorithms according to claim 2,
It is characterized in that:It is configured by Risk Evaluation Factors（300）Allocation models Risk Evaluation Factors to be taken, risk assessment mould
Type training（400）According to the Risk Evaluation Factors Xiang Congcong risk evaluation results library of configuration（100）Obtain training sample data.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110069576A (en) * | 2019-04-26 | 2019-07-30 | 飞叶科技股份有限公司 | A kind of smart city management event is traced to the source algorithm |
| CN110083951A (en) * | 2019-04-30 | 2019-08-02 | 贵州电网有限责任公司 | A kind of solid insulation life-span prediction method based on transformer correlation operation data |
| CN111553613A (en) * | 2020-05-11 | 2020-08-18 | 中石化石油工程技术服务有限公司 | Intelligent grading evaluation method and system for seismic acquisition data quality |
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN111882202A (en) * | 2020-07-24 | 2020-11-03 | 武汉建工集团股份有限公司 | Group building synchronous construction risk management system based on BP neural network |
| CN112184938A (en) * | 2020-08-14 | 2021-01-05 | 广西电网有限责任公司来宾供电局 | Real-time management and reminding system for capital construction operation |
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| CN112365201A (en) * | 2021-01-12 | 2021-02-12 | 江苏巨龙电力工程有限公司 | Neural network-based infrastructure construction monitoring and early warning system and method |
| US11475654B1 (en) | 2020-04-29 | 2022-10-18 | Wells Fargo Bank, N.A. | Technology control evaluation program |
| CN115375113A (en) * | 2022-08-05 | 2022-11-22 | 航天神舟智慧系统技术有限公司 | Overall safety index evaluation method and device for places in primary treatment |
| CN117035431A (en) * | 2023-09-22 | 2023-11-10 | 三峡高科信息技术有限责任公司 | Airport engineering project construction risk assessment method based on artificial intelligence |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050050009A1 (en) * | 2003-08-18 | 2005-03-03 | Martha Gardner | Method and system for assessing and optimizing crude selection |
| CN106096860A (en) * | 2016-06-23 | 2016-11-09 | 国网山东省电力公司经济技术研究院 | A kind of intelligent risk management and control platform |
| CN106469340A (en) * | 2015-08-17 | 2017-03-01 | 苏州优估营网络科技有限公司 | A kind of compound Project Risk Assessment model based on neutral net |
| CN106991531A (en) * | 2017-03-30 | 2017-07-28 | 国家电网公司 | Power distribution network project construction effect Post-assessment Method based on BP neural network |
-
2017
- 2017-11-23 CN CN201711184095.7A patent/CN108241964A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050050009A1 (en) * | 2003-08-18 | 2005-03-03 | Martha Gardner | Method and system for assessing and optimizing crude selection |
| CN106469340A (en) * | 2015-08-17 | 2017-03-01 | 苏州优估营网络科技有限公司 | A kind of compound Project Risk Assessment model based on neutral net |
| CN106096860A (en) * | 2016-06-23 | 2016-11-09 | 国网山东省电力公司经济技术研究院 | A kind of intelligent risk management and control platform |
| CN106991531A (en) * | 2017-03-30 | 2017-07-28 | 国家电网公司 | Power distribution network project construction effect Post-assessment Method based on BP neural network |
Non-Patent Citations (1)
| Title |
|---|
| 曹丽: "基于人工神经网络的工程项目风险管理研究", 《中国优秀硕士学位论文全文数据库》 * |
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| CN110083951B (en) * | 2019-04-30 | 2023-06-02 | 贵州电网有限责任公司 | Solid insulation life prediction method based on relevant operation data of transformer |
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN111582634B (en) * | 2020-03-26 | 2024-02-23 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| US11475654B1 (en) | 2020-04-29 | 2022-10-18 | Wells Fargo Bank, N.A. | Technology control evaluation program |
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| CN111882202A (en) * | 2020-07-24 | 2020-11-03 | 武汉建工集团股份有限公司 | Group building synchronous construction risk management system based on BP neural network |
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| CN112308452A (en) * | 2020-11-18 | 2021-02-02 | 莆田学院 | Power grid project auxiliary decision-making method based on machine learning and storage medium |
| CN112365201A (en) * | 2021-01-12 | 2021-02-12 | 江苏巨龙电力工程有限公司 | Neural network-based infrastructure construction monitoring and early warning system and method |
| CN115375113A (en) * | 2022-08-05 | 2022-11-22 | 航天神舟智慧系统技术有限公司 | Overall safety index evaluation method and device for places in primary treatment |
| CN117035431A (en) * | 2023-09-22 | 2023-11-10 | 三峡高科信息技术有限责任公司 | Airport engineering project construction risk assessment method based on artificial intelligence |
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Application publication date: 20180703 |