CN113850565B - Maturity model-based overall process consultation project management monitoring system and method - Google Patents
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Abstract
The invention provides a maturity model-based overall process consultation project management monitoring system, which comprises: a text information acquisition module; the first semantic analysis module is used for inputting each text data acquired by the text information acquisition module into a first neural network for semantic extraction to obtain a first semantic analysis result; the video information acquisition module is used for acquiring construction information of a site in the construction process; and/or the video information analysis module is used for extracting key frames of the video information, performing enhancement and denoising processing, and then inputting the key frames into a second neural network for semantic analysis to obtain a second semantic analysis result; the server and the database realize accurate judgment and classification of the maturity model through the combined action of the three deep neural networks, avoid the subjectivity of manual judgment and realize automatic judgment.
Description
Technical Field
The invention particularly relates to a maturity model-based overall process consultation project management monitoring system and method, and relates to the fact that a deep learning technology is introduced into judgment of a maturity model to achieve overall process consultation project management monitoring.
Background
The whole-process engineering consultation refers to management services related to each stage of planning consultation, early-stage investigation, engineering design, bidding agency, construction cost consultation, engineering supervision, construction early-stage preparation, construction process management, completion acceptance and operation guarantee and the like in the whole life cycle of the construction engineering. The implementation of the whole-process engineering consultation service is an implementation mode reform of deepening the engineering construction project organization in China, and is an important measure for improving the engineering construction management level, promoting the industry concentration, ensuring the engineering quality and the investment benefit and standardizing the order of the building market. Meanwhile, the method is also the best measure for removing the existing embarrassment of small, scattered, messy and bad in China, such as the adjustment of the operation structure of the existing exploration, design, construction, supervision and other professional enterprises in China, the division of transformation and upgrading are performed, the comprehensive strength is enhanced, the connection with the international construction management service mode is accelerated, and the method is the best measure for removing the existing embarrassment of small, scattered, messy and bad.
Disclosure of Invention
The invention provides a maturity model-based overall process consultation project management monitoring system and a method, wherein the system comprises:
the text information acquisition module is used for acquiring one or more items of text information in a contract, an acceptance standard, a process document, a construction stage and a completion acceptance stage;
the first semantic analysis module is used for inputting each text data acquired by the text information acquisition module into a first neural network for semantic extraction to obtain a first semantic analysis result;
the video information acquisition module is used for acquiring construction information of a site in the construction process;
the video information analysis module is used for extracting key frames of the video information, performing enhancement and denoising processing, and then inputting the key frames into a second neural network for semantic analysis to obtain a second semantic analysis result;
the server is used for judging the maturity level of the management project through the maturity model; the maturity model is a third neural network that receives input including at least results from the first semantic analysis and the second semantic analysis;
and the database is used for storing the text information and the video information which are acquired by the text information acquisition module and the video information acquisition module, and the first semantic analysis result and the second semantic analysis result.
Similarly, the application also correspondingly provides a maturity model-based overall process consultation project management monitoring method, and the method is completed by using the maturity model-based overall process consultation project management monitoring system.
The application also provides an electronic device, which comprises a memory and a processor, wherein the processor is used for executing program instructions, and the instructions are used for executing the overall process consultation item management monitoring method based on the maturity model
The application also provides a computer readable storage medium storing instructions for executing a maturity model-based full-process advisory project management monitoring method
The invention has the beneficial effects that: according to the invention, semantic analysis of text semantic information and video images is respectively input into corresponding semantic analysis networks to obtain semantic information respectively corresponding to the text semantic information and the video images, and finally input into a third neural network to finish judgment of maturity, so that the judgment accuracy is improved, the influence of full-automatic processing and artificial subjective judgment is realized, the three types of neural networks are respectively suitable for different data input and act together, and the problem of improving the judgment precision of maturity is solved.
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Fig. 1 shows the basic constituent structure of the present invention.
Detailed Description
As shown in fig. 1, the present invention provides a maturity model-based overall process consulting project management monitoring system, which includes:
the text information acquisition module is used for acquiring one or more items of text information in a contract, an acceptance standard, a process document, a construction stage and a completion acceptance stage;
the first semantic analysis module is used for inputting the text data acquired by the text information acquisition module into a first neural network for semantic extraction to obtain a first semantic analysis result;
the video information acquisition module is used for acquiring construction information of a site in the construction process;
the video information analysis module is used for extracting key frames of the video information, performing enhancement and denoising processing, and then inputting the key frames into a second neural network for semantic analysis to obtain a second semantic analysis result;
the server is used for judging the maturity level of the management project through the maturity model; the maturity model is a third neural network that receives input including at least results from the first and second semantic analysis;
and the database is used for storing the text information and the video information which are acquired by the text information acquisition module and the video information acquisition module, and the first semantic analysis result and the second semantic analysis result.
Optionally, the maturity corresponds to 1 to 5 for a total of five levels, 0 indicating a preliminary start; 2 denotes delayed progression; 3 represents advance progress; 4, smooth completion acceptance check; 5 indicates that the completion acceptance cannot be achieved; the maturity can also adopt more levels for classification, and the corresponding relation between the number and the specific meaning can be set by self.
Optionally, the first neural network is a BERT network model, the network is trained in a supervised manner, and the model uses a self-attention layer and a storage attention layer to jointly obtain the first semantic information; the processing method of the self-attention layer of the input text comprises the following steps:
A(S,K,V)＝softmax(SK)V
a () represents a function for performing self-attention mechanism processing, softmax () represents a function for performing self-attention mechanism processing by using probability normalization, S is a function for encoding the characteristic of a text key sentence, K is a key sentence index in the text, and V is a projection characteristic of the key sentence in the text;
the stored attention layer processes the input text with a self-attention mechanism as follows,
M(S,Mk,Mv)＝softmax(S·Mk)Mv
wherein, M () represents the result of memory attention mechanism processing, Softmax () represents the function of probability normalization used by the memory attention mechanism processing, S represents the coding feature of the key sentence in the text, M () represents the result of the memory attention mechanism processingKFor indexing key sentences in text, MVThe projection features of the key sentences in the text.
Optionally, the second neural network is a recursive deep neural network, and the recursive deep neural network includes: the system comprises an information input layer, at least one convolution layer, at least one pooling layer, at least one hidden layer and a full-connection layer; the convolution kernel size adopted by the convolution layer is 3 x 3; the pooling layer is calculated by a maximum pooling method; the recurrent neural network adopts an excitation function, which is denoted as f (), wherein
Wherein, thetayiDenoted as sample i and its corresponding label yiThe vector included angle of (A); the N represents the number of training samples; said wyiIndicating that sample i is at its label yiThe weight of (c).
Optionally, the second neural network is a deep convolutional neural network, and specifically includes: the device comprises an input layer, an embedded layer, at least one pooling layer and a full-connection layer; the input layer is used for receiving key frame images; the convolution kernel adopted by the embedding layer has the size of 5 x 5; the excitation function is a sigmoid function, and semantic analysis results are further obtained after full connection layer processing;
the pooling layer pooling method comprises the following steps:
xe＝f(1-φ(ue))
ue＝weφ(xe-1) (ii) a Wherein the content of the first and second substances,xerepresents the output of the current layer, ueRepresenting an excitation function RlThe input of (a) is performed,
Rl() Representing an excitation function, weRepresents the weight of the current layer, phi represents the loss function, xe-1Representing the output of the previous layer.
Optionally, the third neural network is a deep convolutional neural network, and specifically includes: an input layer, a convolution layer, a pooling layer, a full-connection layer; the input layer is used for receiving first semantic information and second semantic information; the convolution kernel size adopted by the convolution layer is 3 x 3; the excitation function is noted as Rl() (ii) a After the treatment of the full connection layer, further obtaining a maturity judgment result;
the pooling layer adopts a pyramid pooling method;
excitation function RlComprises the following steps:
n represents the size of the positive sample data set, i takes values of 1-N, yiRepresents a positive sample xiA corresponding tag value; wyiRepresenting a positive sample feature vector xiAt its label yiThe weight is the weight, and s is a judgment parameter of the deep convolutional neural network; bjRepresents a sample xiAt its label yiThe deviation of (a).
The invention also provides a maturity model-based overall process consultation project management monitoring method, which is completed by using any one of the maturity model-based overall process consultation project management monitoring systems.
The present invention also proposes an electronic device comprising a memory and a processor for executing program instructions for performing any of the methods described above.
The invention also provides a computer-readable storage medium for storing instructions corresponding to any one of the methods.
The instructions described herein may be downloaded from a computer-readable storage medium to various computing/processing power supply devices, or to an external computer or external storage power supply device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing power supply device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing power supply device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other power generation devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other power providing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other power providing devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other power providing devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (3)
1. A maturity model based full process advisory project management monitoring system, the system comprising:
the text information acquisition module is used for acquiring text information in a contract, an acceptance standard, a process document, a construction stage and a completion acceptance stage;
the first semantic analysis module is used for inputting each text data acquired by the text information acquisition module into a first neural network for semantic extraction to obtain a first semantic analysis result;
the video information acquisition module is used for acquiring construction information of a site in the construction process;
the video information analysis module is used for extracting key frames of the video information, performing enhancement and denoising processing, and then inputting the key frames into a second neural network for semantic analysis to obtain a second semantic analysis result;
the server is used for judging the maturity level of the management project through the maturity model; the maturity model is a third neural network that receives input including at least results from the first and second semantic analysis;
the database is used for storing the text information and the video information which are acquired by the text information acquisition module and the video information acquisition module, and the first semantic analysis result and the second semantic analysis result;
the maturity corresponds to 1 to 5 for a total of five levels, 0 indicating the initial start; 2 denotes delayed progression; 3 represents advance progress; 4, smooth completion acceptance check; 5 indicates that the completion acceptance cannot be achieved;
the first neural network is a BERT network model, the network is trained in a supervision mode, and the model adopts a self-attention layer and a storage attention layer to jointly obtain first semantic information; the processing method of the self-attention layer of the input text comprises the following steps:
A(S,K,V)＝softmax(SK)V
a () represents a function for performing self-attention mechanism processing, softmax () represents a function for performing self-attention mechanism processing by using probability normalization, S is a function for encoding the characteristic of a text key sentence, K is a key sentence index in the text, and V is a projection characteristic of the key sentence in the text;
the stored attention layer processes the input text with a self-attention mechanism as follows,
M(S,Mk,Mv)＝softmax(S·Mk)Mv
wherein, M () represents the result of memory attention mechanism processing, Softmax () represents the function of probability normalization used by the memory attention mechanism processing, S represents the coding feature of the key sentence in the text, M () represents the result of the memory attention mechanism processingKFor indexing key sentences in text, MVThe projection features of the key sentences in the text.
2. The system for managing and monitoring the whole process consultation items based on the maturity model as claimed in claim 1, wherein the second neural network is a deep convolution neural network, and specifically comprises: the device comprises an input layer, an embedded layer, at least one pooling layer and a full-connection layer; the input layer is used for receiving key frame images; the convolution kernel size adopted by the embedding layer is 5 x 5; the excitation function is a sigmoid function, and semantic analysis results are further obtained after full connection layer processing;
the pooling method of the pooling layer comprises the following steps:
xe＝f(1-φ(ue))
ue＝weφ(xe-1) (ii) a Wherein x iseRepresents the output of the current layer, ueRepresenting an excitation function RlThe input of (2) is performed,
Rl() Representing an excitation function, weRepresents the weight of the current layer, phi represents the loss function, xe-1Representing the output of the previous layer.
3. The system according to claim 1, wherein the third neural network is a deep convolutional neural network, and specifically comprises: an input layer, a convolution layer, a pooling layer, a full-connection layer; the input layer is used for receiving first semantic information and second semantic information; the convolution kernel size adopted by the convolution layer is 3 x 3; the excitation function is noted as Rl() (ii) a After the full connection layer treatment, further obtaining a maturity judgment result;
the pooling layer adopts a pyramid pooling method;
excitation function RlComprises the following steps:
n represents the size of the positive sample data set, i takes values of 1-N, yiRepresents a positive sample xiA corresponding tag value; wyiRepresenting a positive sample feature vector xiAt its label yiThe weight of the position is set, and s is a judgment parameter of the deep convolutional neural network; bjRepresents a sample xiAt its label yiThe deviation of (a).
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Effective date of registration: 20220802 Address after: 528200 floor 12-13, No. 7, Guicheng South Third Road, Nanhai District, Foshan City, Guangdong Province (residence declaration) Patentee after: Guangdong Chengyu Engineering Consulting and Supervision Co.,Ltd. Patentee after: FOSHAN POWER SUPPLY BUREAU OF GUANGDONG POWER GRID Co.,Ltd. Address before: 528200 floor 12-13, No. 7, Guicheng South Third Road, Nanhai District, Foshan City, Guangdong Province (residence declaration) Patentee before: Guangdong Chengyu Engineering Consulting and Supervision Co.,Ltd. |