CN113505217A - Method and system for realizing rapid formation of project cost database based on big data - Google Patents
Method and system for realizing rapid formation of project cost database based on big data Download PDFInfo
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Abstract
The invention discloses a method and a system for realizing the rapid formation of a project cost database based on big data, wherein the method comprises the following steps: respectively acquiring at least one reference document file related to each of majors related to construction cost, wherein the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, elevator and lightning protection grounding; respectively carrying out named entity recognition on each professional reference document file; searching a document file associated with a corresponding specialty according to the named entity of each specialty; respectively carrying out named entity recognition on each professional document file; and screening each professional document file according to the identified named entities, and storing the document files in a professional manner to form a database. By adopting the method, the construction of each professional document file of the engineering cost can be quickly realized, and the files are stored in the professional areas and are convenient to search.
Description
Technical Field
The invention belongs to the technical field of database construction, and particularly relates to a method and a system for realizing quick formation of a project cost database based on big data.
Background
According to the national standard of Project cost terminology (GB/T50875-2013) issued by the Ministry of urban and rural construction of housing, Project Costs (PC) refer to the construction Costs expected or actually paid out during the construction period of a Project. According to the national standard of Project cost terminology (GB/T50875-2013) issued by the Ministry of urban and rural construction of housing, Project Costs (PC) refer to the construction Costs expected or actually paid out during the construction period of a Project.
At present, the informatization overall level of the engineering and building field in China is relatively low, especially in the engineering cost information industry with complex data. Most construction enterprises and construction workers also stay and depend on experience accumulation of people and tabular storage of a common computer. Under the condition that various industry standards and laws and regulations are successively issued by national governing departments and the industry development is more and more standard, effective cost data management and storage are carried out only by depending on the experience and knowledge of cost workers of construction enterprises, which is a particularly troublesome thing, and target information cannot be searched manually in massive cost information data. Therefore, the data intelligence library greatly improves the manufacturing cost efficiency of the user.
Construction costs involve multiple specialties, which involve large amounts of data, and how to quickly build a database of construction costs is a matter of considerable research.
Disclosure of Invention
In order to solve the existing problems, the invention provides a method and a system for realizing the rapid formation of a project cost database based on big data, which can rapidly realize the construction of each professional document file of the project cost and are convenient to search by professional storage.
The invention is realized by the following technical scheme:
the invention provides a method for realizing the rapid formation of a project cost database based on big data, which comprises the following steps:
respectively acquiring at least one reference document file related to each of majors related to construction cost, wherein the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, elevator and lightning protection grounding;
respectively carrying out named entity recognition on each professional reference document file;
searching a document file associated with a corresponding specialty according to the named entity of each specialty;
respectively carrying out named entity recognition on each professional document file;
and screening each professional document file according to the identified named entities, and storing the document files in a professional manner to form a database.
According to the method, named entities in reference document files related to each specialty are identified, more document files related to the corresponding specialty are searched through the named entities, the named entities in the document files are identified, the document files are screened based on the identified named entities, and the document files related to each specialty are finally determined.
In one possible design, the searching the document file associated with each professional named entity according to the named entity comprises:
obtaining local document files related to corresponding specialties according to named entities of each speciality; and/or the presence of a gas in the gas,
and crawling the document files related to the corresponding specialties from the network according to the named entities of each speciality.
In one possible design, the named entity identifying each specialized reference document file separately includes:
identifying and classifying named entities of each professional reference document file;
and judging the relevance between the classified named entity and the specialty to determine to screen out the reference named entity corresponding to the specialty.
In one possible design, the screening of each specialized document file according to the identified named entities includes:
respectively counting the named entity types identified by each document file and the corresponding number of the named entity types;
and screening out the document files with the same named entity types as the reference named entity and the number of the named entity types larger than a threshold value to obtain the document file corresponding to each specialty.
In one possible design, the screening each professionally crawled document file according to the identified named entities further comprises:
searching the identity of all professional document files, and searching document files corresponding to at least two professionals simultaneously;
and determining the professions which correspond to the at least two professions simultaneously and finally corresponding to the document files, and adding labels on the document files, wherein the labels comprise professional labels except the professions which correspond finally.
The second aspect of the present invention provides a system for implementing rapid formation of a project cost database based on big data, comprising:
the system comprises a document file acquisition unit, a document file acquisition unit and a data processing unit, wherein the document file acquisition unit is used for respectively acquiring at least one reference document file related to each of the majors related to the construction cost, and the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, an elevator and lightning protection grounding;
the named entity recognition unit is used for carrying out named entity recognition on each professional document file;
the document file searching unit is used for searching document files related to corresponding specialties according to named entities of each speciality;
the document file screening unit is used for screening each professional document file according to the identified named entities;
and the database construction unit is used for performing professional storage on the text documents according to the screening result of the document file screening unit to form a database.
In one possible design, the document file searching unit includes:
the local searching unit is used for acquiring local document files related to corresponding specialties according to named entities of each speciality;
and the network data crawling unit is used for crawling the document files related to the corresponding specialties from the network according to the named entities of each speciality.
In one possible design, the database construction unit includes:
the data statistics unit is used for respectively counting the named entity types identified by each document file and the corresponding number of the named entity types;
the screening unit is used for screening out the document files with the same named entity types as the reference named entity and the number of the named entity types larger than a threshold value to obtain the document files corresponding to each specialty;
and the construction unit is used for performing professional storage on the document files to form a database.
Compared with the prior art, the invention at least has the following advantages and beneficial effects:
the method can rapidly realize the construction of each professional document file of the engineering cost by identifying the named entity in each professional related reference document file, searching more document files related to the corresponding profession through the named entity, identifying the named entity in the document files, screening the document files based on the identified named entity and finally determining each professional related document file.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a flow chart of the method for implementing rapid formation of a project cost database based on big data according to the present invention.
FIG. 2 is a schematic block diagram of a system for implementing rapid formation of a project cost database based on big data according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It should be understood that, for the term "and/or" as may appear herein, it is merely an associative relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time; for the term "/and" as may appear herein, which describes another associative object relationship, it means that two relationships may exist, e.g., a/and B, may mean: a exists independently, and A and B exist independently; in addition, for the character "/" that may appear herein, it generally means that the former and latter associated objects are in an "or" relationship.
It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
The invention discloses a method for realizing the rapid formation of a project cost database based on big data, which can be executed by a construction device, wherein the construction device can be software or the combination of the software and the hardware, and the construction device can be integrated in a server, a terminal device and the like. Specifically, as shown in fig. 1, the method for quickly forming the project cost database includes the following steps S101 to S105.
Step S101, respectively obtaining at least one reference document file related to each of the majors related to the construction cost, wherein the majors at least comprise decoration, building, heating, water supply and drainage, fire protection, lighting, weak current, power, elevator and lightning protection grounding.
In this step, the more reference documents per specialty, the more named entities corresponding to the specialty are subsequently identified, and the more accurate the screening and categorization of the document files. If only one reference document file exists in a single professional, the selection of the reference document file needs to be paid attention to in order to improve the accuracy of screening and classifying the document files.
And S102, respectively carrying out named entity recognition on the reference document file of each specialty to obtain a reference named entity corresponding to each specialty. The named entities comprise entities, attributes and the like, the entities comprise masonry structures, steel structures, building decoration, indoor decoration and the like, and the attributes comprise engineering quantity, unit price, grade and the like. Each professional reference document may be one or more, and when there are a plurality of reference documents, the named entities of each reference document may be repeated, and the named entities of each professional reference document file need to be classified firstly, that is, the named entities of the same reference documents are classified; and judging the relevance between the classified named entity and the specialty again to determine to screen out the reference named entity corresponding to the specialty.
In this step, a named entity recognition model composed of a pre-trained language model Bert and a conditional random field CRF may be used for recognition, and the model needs to be a pre-trained model.
And step S103, searching a document file associated with each specialty according to the reference named entity corresponding to each specialty. Document files are sourced from a variety of sources, such as local files or network files. The network file is a source of big data, where the searching includes local searching and/or network acquisition, that is, specifically, the steps include: obtaining local document files related to corresponding specialties according to named entities of each speciality; and/or crawling document files related to the corresponding specialties from the network according to the named entities of each speciality. The source of data crawled from the website is open data.
And step S104, respectively carrying out named entity recognition on the document files associated with each specialty. In this step, the named entity recognition model composed of the pre-training language model Bert and the conditional random field CRF may also be used for recognition, which is not described herein again.
And S105, screening the document files related to each specialty respectively according to the identified named entities, and storing the document files in the specialties to form a database.
Specifically, the method comprises the following steps: respectively counting the named entity types identified by each document file and the corresponding number of the named entity types; the document file searched by the named entity is not necessarily the document text of the present technology, and the confirmation of the searching accuracy is realized by the named entity. Further, the document files with the same named entity types as the reference named entities and the number of the named entity types larger than a threshold value are screened out, and the document files corresponding to each specialty are obtained.
However, the screened document files corresponding to each specialty may be duplicated, that is, one document file belongs to both specialty a and specialty B, and corresponds to at least two specialties, and the duplicated storage would result in waste of storage space, and in order to solve the above problem, the method further includes: searching the identity of all professional document files, and searching document files corresponding to at least two professionals simultaneously; and determining the professions finally corresponding to the document files simultaneously corresponding to the at least two professions, and adding labels on the document files, wherein the labels comprise professional labels except the professions finally corresponding to the professions, so that the subsequent use and search are facilitated.
The method adopts the steps S101 to S105, searches more document files related to the corresponding profession through the named entities by identifying the named entities in the reference document files related to each profession, and finally determines the document files related to each profession through identifying the named entities in the document files and screening the document files based on the identified named entities.
A second aspect of the present invention provides a system for implementing fast formation of a project cost database based on big data, which is to implement the method in the first aspect, as shown in fig. 2, and the system includes:
the system comprises a document file acquisition unit, a document file acquisition unit and a data processing unit, wherein the document file acquisition unit is used for respectively acquiring at least one reference document file related to each of the majors related to the construction cost, and the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, an elevator and lightning protection grounding;
the named entity recognition unit is used for carrying out named entity recognition on each professional document file;
the document file searching unit is used for searching document files related to corresponding specialties according to named entities of each speciality;
the document file screening unit is used for screening each professional document file according to the identified named entities;
and the database construction unit is used for performing professional storage on the text documents according to the screening result of the document file screening unit to form a database.
The connection relationship among the document file obtaining unit, the named entity identifying unit, the document file searching unit, the document file screening unit and the database constructing unit is shown in fig. 2, and the signal flow thereof is shown with reference to the method of the first aspect of the present invention.
Specifically, the document file searching unit includes:
the local searching unit is used for acquiring local document files related to corresponding specialties according to named entities of each speciality;
and the network data crawling unit is used for crawling the document files related to the corresponding specialties from the network according to the named entities of each speciality.
The database construction unit includes:
the data statistics unit is used for respectively counting the named entity types identified by each document file and the corresponding number of the named entity types;
the screening unit is used for screening out the document files with the same named entity types as the reference named entity and the number of the named entity types larger than a threshold value to obtain the document files corresponding to each specialty;
and the construction unit is used for performing professional storage on the document files to form a database.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: modifications may be made to the embodiments described above, or equivalents may be substituted for some of the features described. And such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for realizing the rapid formation of a project cost database based on big data is characterized by comprising the following steps:
respectively acquiring at least one reference document file related to each of majors related to construction cost, wherein the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, elevator and lightning protection grounding;
respectively carrying out named entity recognition on the reference document file of each specialty to obtain a reference named entity corresponding to each specialty;
searching a document file associated with each specialty according to the reference named entity corresponding to each specialty;
respectively carrying out named entity recognition on each professional associated document file;
and screening the document files associated with each specialty respectively according to the identified named entities, and storing the document files in the specialties to form a database.
2. The method of claim 1, wherein the searching the document file associated with each professional named entity according to the named entity comprises:
obtaining local document files related to corresponding specialties according to named entities of each speciality; and/or the presence of a gas in the gas,
and crawling the document files related to the corresponding specialties from the network according to the named entities of each speciality.
3. The method for realizing the rapid formation of the project cost database based on the big data as claimed in claim 1, wherein the identifying named entities for each professional reference document file respectively comprises:
identifying and classifying named entities of each professional reference document file;
and judging the relevance between the classified named entity and the specialty to determine to screen out the reference named entity corresponding to the specialty.
4. The method for implementing rapid formation of project cost database based on big data as claimed in claim 1, wherein said screening each professional document file according to the identified named entities comprises:
respectively counting the named entity types identified by each document file and the corresponding number of the named entity types;
and screening out the document files with the same named entity types as the reference named entity and the number of the named entity types larger than a threshold value to obtain the document file corresponding to each specialty.
5. The method of claim 4, wherein the step of screening each of the professionally crawled document files according to the identified named entities further comprises:
searching the identity of all professional document files, and searching document files corresponding to at least two professionals simultaneously;
and determining the professions which correspond to the at least two professions simultaneously and finally corresponding to the document files, and adding labels on the document files, wherein the labels comprise professional labels except the professions which correspond finally.
6. A system for realizing rapid formation of a project cost database based on big data is characterized by comprising:
the system comprises a document file acquisition unit, a document file acquisition unit and a data processing unit, wherein the document file acquisition unit is used for respectively acquiring at least one reference document file related to each of the majors related to the construction cost, and the related majors at least comprise decoration, building, heating, water supply and drainage, fire fighting, lighting, weak current, power, an elevator and lightning protection grounding;
the named entity recognition unit is used for carrying out named entity recognition on each professional document file;
the document file searching unit is used for searching document files related to corresponding specialties according to named entities of each speciality;
the document file screening unit is used for screening each professional document file according to the identified named entities;
and the database construction unit is used for performing professional storage on the text documents according to the screening result of the document file screening unit to form a database.
7. The system for realizing the rapid formation of the project cost database based on the big data as claimed in claim 6, wherein the document file searching unit comprises:
the local searching unit is used for acquiring local document files related to corresponding specialties according to named entities of each speciality;
and the network data crawling unit is used for crawling the document files related to the corresponding specialties from the network according to the named entities of each speciality.
8. The system for realizing the rapid formation of the project cost database based on big data as claimed in claim 6, wherein the database construction unit comprises:
the data statistics unit is used for respectively counting the named entity types identified by each document file and the corresponding number of the named entity types;
the screening unit is used for screening out the document files with the same named entity types as the reference named entity and the number of the named entity types larger than a threshold value to obtain the document files corresponding to each specialty;
and the construction unit is used for performing professional storage on the document files to form a database.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115934880A (en) * | 2022-10-31 | 2023-04-07 | 永道工程咨询有限公司 | Construction of project cost document database and search method of project cost document |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901235A (en) * | 2009-05-27 | 2010-12-01 | 国际商业机器公司 | Method and system for document processing |
| CN105989546A (en) * | 2016-04-13 | 2016-10-05 | 国网吉林省电力有限公司经济技术研究院 | Power transmission and transformation construction cost basic data collection and analysis system |
| CN110457532A (en) * | 2019-07-24 | 2019-11-15 | 深圳市华阳国际工程造价咨询有限公司 | A kind of method for building up and system of cost consultation database |
| CN110546633A (en) * | 2017-04-25 | 2019-12-06 | 微软技术许可有限责任公司 | Named entity based category tag addition for documents |
| US20200175080A1 (en) * | 2018-12-04 | 2020-06-04 | Sap Se | Digital documents in a distributed storage environment |
| CN112732897A (en) * | 2020-12-28 | 2021-04-30 | 平安科技（深圳）有限公司 | Document processing method and device, electronic equipment and storage medium |
-
2021
- 2021-07-29 CN CN202110866856.7A patent/CN113505217A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901235A (en) * | 2009-05-27 | 2010-12-01 | 国际商业机器公司 | Method and system for document processing |
| CN105989546A (en) * | 2016-04-13 | 2016-10-05 | 国网吉林省电力有限公司经济技术研究院 | Power transmission and transformation construction cost basic data collection and analysis system |
| CN110546633A (en) * | 2017-04-25 | 2019-12-06 | 微软技术许可有限责任公司 | Named entity based category tag addition for documents |
| US20200175080A1 (en) * | 2018-12-04 | 2020-06-04 | Sap Se | Digital documents in a distributed storage environment |
| CN110457532A (en) * | 2019-07-24 | 2019-11-15 | 深圳市华阳国际工程造价咨询有限公司 | A kind of method for building up and system of cost consultation database |
| CN112732897A (en) * | 2020-12-28 | 2021-04-30 | 平安科技（深圳）有限公司 | Document processing method and device, electronic equipment and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115934880A (en) * | 2022-10-31 | 2023-04-07 | 永道工程咨询有限公司 | Construction of project cost document database and search method of project cost document |
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Application publication date: 20211015 |