CN111400430A - Method and system for quickly combining prices in digital building list pricing - Google Patents
Method and system for quickly combining prices in digital building list pricing Download PDFInfo
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Abstract
The invention discloses a method for quickly grouping prices in the pricing of a digital building list, which is used for processing data from various sources, and comprises data collection and data application, wherein the data collection comprises data analysis and warehousing and index establishment, engineering project establishment, uploading of data after storage space allocation, big data analysis after the analysis of data in various formats and index database establishment; the data application comprises single recommendation and batch automatic extraction, the group price data is matched according to the list information, the result after the data application is stored can be stored, and filtering and item-by-item inspection are carried out according to the result. The invention combines multiple data sources for trial use, ensures the quality of industrial data and expert data, ensures that a user can efficiently and quickly find the most matched data, and improves the group price efficiency; the data source can be selected independently, the result list can be filtered in multiple dimensions, and the efficiency of leakage detection and defect filling is improved.
Description
Technical Field
The invention belongs to the technical field of computer information, mainly aims at compiling bidding engineering quantity lists, bidding control prices and bidding quotation related projects of a building industry bidding link, and particularly relates to a method and a system for quickly and accurately pricing in pricing software list pricing projects.
Background
In the establishment of the bidding and pricing project, the list pricing is a universal establishment method at present, and the establishment of the pricing project is completed by sleeving quotations periodically issued by residence and construction committees in various regions under a project quantity list through pricing software and setting related contents, project quantities and prices. Along with the lapse of time, the enterprises or individuals in the construction cost industry participate in the compilation of more and more list pricing projects, more and more similar lists are encountered, and some similar and acquainted lists are always encountered during the project compilation, but the places where the similar and acquainted lists are done cannot be found. The compiling personnel need to go to the computer, the U disk, the network disk and the like of the compiling personnel to search and open the project one by one, then compare the item characteristics one by one, find out the list in the impression of the compiling personnel and carry out quota pricing multiplexing in a copying and pasting or batch import mode. The operation process is very complicated, the success rate is extremely low, and most of the compiling personnel still apply the quota again to carry out pricing.
The existing product reuses historical quota group price data, more on the basis of finding a historical project, simple fuzzy matching is carried out on list codes, names and project characteristics through a lead-in project, quota group prices on matching are reused, and a detailed flow is shown in fig. 1.
The existing reuse of historical engineering quota group price data still has a plurality of defects.
1. The data source is single, the data of the historical project can only be multiplexed, and the data cannot be managed and maintained uniformly;
2. historical projects are not managed uniformly, storage is dispersed, and searching difficulty is high;
3. the list matching rule is simple, and the accuracy is low;
4. the operation flow is complex and the efficiency is low.
Disclosure of Invention
Aiming at the defects of the background art, the invention aims to solve the problem of how to realize intelligent and rapid extraction of the form.
In order to achieve the above objects, the present invention provides a method for rapid pricing in digital building inventory pricing, which processes data from a variety of sources, including data collection and data application, wherein,
the data collection comprises data analysis and storage and index establishment, engineering project establishment, data uploading after storage space allocation, big data analysis after analysis of various format data and index database establishment;
the data application comprises single recommendation and batch automatic extraction, the group price data is matched according to the list information, the result after the data application is stored can be stored, and filtering and item-by-item inspection are carried out according to the result.
Preferably, the data collection is divided into an online collection and an offline collection according to the source.
Preferably, the data collection specifically includes:
step 1.1, establishing an engineering project and configuring a storage area;
step 1.2, processing and uploading stored data;
step 1.3, analyzing the multi-source multi-format data;
step 1.4, performing big data analysis by using machine learning;
and step 1.5, indexing the data and establishing an index library.
Preferably, the data application includes:
2.1, selecting a data source to carry out single recommendation;
step 2.2, matching and batch automatic extraction are carried out on the list pricing schemes;
and 2.3, establishing a search system to realize index retrieval and query.
Preferably, after the result is obtained in step 2.1, the matched list name, features, sub-directory basic information, work and material machine basic information, tags, data sources and other information are presented.
Preferably, in step 2.2, the highest-matching-degree list pricing scheme is selected each time, pricing result information of each time is saved, a subsequent check interface is provided, and data filtering is performed according to the pricing result information.
Preferably, the result information of each pricing includes a data source and a matching degree, and the matching degree is standard, similar and empty.
Preferably, in step 2.3, the uploaded list name and item feature information are normalized based on the knowledge base by using a machine learning algorithm, the name and item feature are segmented, a search is performed in the inverted index according to the segmentation result, and a result with the highest matching degree is output.
A system for rapid pricing in digital building inventory pricing, processing a plurality of source numbers, comprising:
the data collection unit is used for analyzing data, warehousing and establishing indexes, establishing engineering projects, uploading data after allocating storage space, analyzing various format data, analyzing big data and establishing an index library;
and the data application unit is used for single recommendation and batch automatic extraction, matching the group price data according to the list information, storing the result after the data application is stored, and filtering and checking item by item according to the result.
Preferably, the data collection unit is divided into an online collection and an offline collection according to the source, and includes:
the project building and storing module is used for building engineering projects and configuring a storage area;
the data processing and uploading module is used for processing and uploading the stored data;
the multivariate data analysis module is used for analyzing the multisource and multiformat data;
the big data analysis module is used for carrying out big data analysis by utilizing machine learning;
and the data indexing and indexing module is used for indexing data and establishing an index library.
Preferably, the data application unit includes:
the single recommending module is used for selecting a data source to recommend the single item;
the matching and extracting module is used for matching and automatically extracting the list pricing schemes in batches;
and the search query module is used for establishing a search system and realizing index retrieval query.
Preferably, after the single recommending module obtains the result, the list name, the characteristics, the sub-category basic information, the work and material machine basic information, the label, the data source and other information on the matching are presented.
Preferably, the matching and extracting module selects the highest-matching-degree clear group price scheme each time, stores price result information of each time, provides a subsequent check interface, and performs data filtering according to the price result information.
Preferably, the search query module performs normalization processing on the uploaded list name and item feature information based on a knowledge base by using a machine learning algorithm, performs word segmentation on the name and item feature, searches in the inverted index according to word segmentation results, and outputs a result with the highest matching degree.
Compared with the prior art, the invention has the following advantages:
1. the multiple data sources are tried in combination, so that a user can conveniently and quickly complete the price according to the requirement;
2. the quality of industrial data and expert data can be guaranteed and the best quality data can be provided for users based on the data analysis of big data, machine learning and a knowledge base;
3. based on the data matching of a search engine, Chinese segmentation and a segmentation library, the user can efficiently and quickly find the most matched data, and the group price efficiency is improved;
4. the method provides a single recommendation and batch automatic extraction mode, and can autonomously select data sources, so that a user can use recommended group price data in various different scenes;
5. the result back-checking function is provided, the result list can be filtered in multiple dimensions, and the efficiency of checking omission and filling is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 shows a prior art flow diagram;
FIG. 2 is a flow chart illustrating a method for fast pricing in digital building inventory pricing according to the present invention;
FIG. 3 shows a data collection flow diagram of the present invention;
fig. 4 shows a data application flow diagram of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 2, the present embodiment provides a method for fast pricing in digital building inventory pricing, which processes data from a variety of sources, including data collection and data application, wherein,
in some embodiments, the data collection comprises data analysis and storage and index establishment, engineering project establishment, data uploading after storage space allocation, big data analysis after analysis of data in various formats and index establishment;
in some embodiments, the data application includes single recommendation and batch automatic extraction, the group price data is matched according to the list information, the result after the data application is stored, and filtering and item-by-item checking are performed according to the result.
In some embodiments, data collection is divided into online collection and offline collection based on source.
In some embodiments, the data collection specifically includes:
step 1.1, establishing an engineering project and configuring a storage area;
step 1.2, processing and uploading stored data;
step 1.3, analyzing the multi-source multi-format data;
step 1.4, performing big data analysis by using machine learning;
and step 1.5, indexing the data and establishing an index library.
In some embodiments, as shown in fig. 3, the detailed data collection process specifically includes:
1. and (4) preservation engineering: the personal space of the current user in the cloud or the enterprise space of the enterprise to which the current user belongs can be selected, the fragment uploading technology is adopted for uploading, and breakpoint continuous transmission can be carried out.
2. Data archiving: data information is packaged into a json format at a client side and uploaded to a cloud big data storage system, the big data analysis system analyzes the json file to generate detailed group price data, and relevant data are written into a result database.
3. The method comprises the steps of industry/expert data analysis, wherein raw data support multiple formats including but not limited to Excel, XM L, engineering files and the like, the raw data are uploaded to a cloud big data storage system, a big data analysis system is used for analysis in the analysis process, the raw data are analyzed into data packets in a json format, then the data packets are analyzed into detailed group price data, the detailed group price data are written into a corresponding relational database, the analysis of the json data packets and the previous step share the same set of logic, and only the target database is different.
4. Big data analysis: in the process, a machine learning technology is introduced, feature extraction is carried out on the list name and the item features based on an accumulated knowledge base to form a normalized feature description, and the deduplication is more accurate on the basis of the normalized feature description.
5. Establishing an index: extracting basic information of lists such as codes, names, units, project characteristics and the like from a project or a database, using a full-text retrieval technology to segment the information and establish an inverted index to be written into a full-text retrieval system (namely a search engine), wherein the index points to the detailed position of pricing data in the project or the primary key ID of the pricing data in a result database; the word segmentation technology uses an IKanalyer Chinese word segmentation device, a word segmentation library is a service word library summarized by an internal expert, and information participating in word segmentation is name and project characteristics.
As shown in fig. 4, in some embodiments, the data application includes:
2.1, selecting a data source to carry out single recommendation;
step 2.2, matching and batch automatic extraction are carried out on the list pricing schemes;
and 2.3, establishing a search system to realize index retrieval and query.
In some embodiments, the data application includes single recommendation and batch automatic extraction, the main process is to find the most matched group price data according to the list information, the results (data source, tag information and matching degree) after the data application are stored, and filtering and item-by-item checking can be performed according to the results in the later period.
In some embodiments, after the results are obtained in step 2.1, the matched list names, features, sub-directory basic information, work and material machine basic information, tags, data sources and other information are presented.
In some embodiments, step 2.2 selects the highest matching ranking scheme each time, saves the ranking result information each time, provides a subsequent check interface, and performs data filtering according to the ranking result information.
In some embodiments, the result information of each group price comprises a data source and a matching degree, and the matching degree is classified as standard, similar and empty.
In some embodiments, step 2.3 uses a machine learning algorithm to perform normalization processing on the uploaded list name and item feature information based on the knowledge base, performs word segmentation on the name and item feature, searches in the inverted index according to the word segmentation result, and outputs a result with the highest matching degree.
The invention also provides an embodiment of a system for rapid pricing in digital building inventory pricing, which processes a plurality of source numbers, and the system comprises:
the data collection unit is used for analyzing data, warehousing and establishing indexes, establishing engineering projects, uploading data after allocating storage space, analyzing various format data, analyzing big data and establishing an index library;
and the data application unit is used for single recommendation and batch automatic extraction, matching the group price data according to the list information, storing the result after the data application is stored, and filtering and checking item by item according to the result.
In some embodiments, the data collection unit is divided into an online collection and an offline collection according to the source, including:
the project building and storing module is used for building engineering projects and configuring a storage area;
the data processing and uploading module is used for processing and uploading the stored data;
the multivariate data analysis module is used for analyzing the multisource and multiformat data;
the big data analysis module is used for carrying out big data analysis by utilizing machine learning;
and the data indexing and indexing module is used for indexing data and establishing an index library.
In some embodiments, the data application unit comprises:
the single recommending module is used for selecting a data source to recommend the single item;
the matching and extracting module is used for matching and automatically extracting the list pricing schemes in batches;
and the search query module is used for establishing a search system and realizing index retrieval query.
In some embodiments, after the single recommendation module obtains the results, the list name, characteristics, sub-directory basic information, work machine basic information, tags, data sources, and other information on the match are presented.
In some embodiments, the matching extraction module selects the highest matching degree list price scheme each time, stores price result information of each time, provides a subsequent check interface, and performs data filtering according to the price result information.
In some embodiments, the search query module performs normalization processing on the uploaded list name and item feature information based on a knowledge base by using a machine learning algorithm, performs word segmentation on the name and item feature, searches in the inverted index according to word segmentation results, and outputs a result with the highest matching degree.
In some embodiments, the data sources are rich: the user can independently select and use data from various sources, and sorting selection is carried out according to the matching accuracy.
a) History engineering: historical engineering is managed in a cloud storage mode, each user and each enterprise have an exclusive space in the cloud, and as long as the engineering is stored in the cloud, the engineering does not need to be searched everywhere, and engineering data in the space can be directly reused.
b) Archiving data: the user carries out cloud storage aiming at experience quota group price data of some competitive products, and can multiplex at any time and any place.
c) Providing industry big data: and a big data + machine learning mode is introduced to provide professional data for the user.
d) Providing cost association expert data: and the system is cooperated with part of cost associations, converts some recommended use modes of experts in the organization into rated group price data, and recommends the rated group price data for users.
In some embodiments, the matching rules are more scientifically accurate: a search engine technology and a professional word segmentation technology are introduced, a search index is established through a big data + cloud computing technology, and the most similar list can be found more accurately.
In some embodiments, the data sources are not limited to a single source: the first one has the higher matching priority of the four sources, and the user can also select part of the data sources for data multiplexing.
In some embodiments, the interaction is simpler and easier to use: the use scene of recommending group price and batch multiplexing for a single list is provided, the cloud end is automatically matched according to the list information, and the use is simple and efficient.
In some embodiments, the group-price multiplexing results are visualized: and recording the group price information (source, accuracy, label, engineering path and the like) of each list and carrying out visual display, and carrying out classification check on the filtering interface list by a user according to the information.
Furthermore, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Furthermore, a server may be provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
Compared with the prior art, the invention has the following advantages:
1. a user can independently select and use data from various sources, and sorting and selection are performed according to matching accuracy;
2. the matching rules are more scientific and accurate, a search engine technology, a professional word segmentation technology and a word segmentation library are introduced, data analysis is carried out through a big data technology to establish a search index, and the most similar list can be found more accurately;
3. the data sources are not limited to a single source: the four sources in the first item have higher matching degree and take precedence, and the user can also autonomously select partial data sources for data multiplexing;
4. the interaction is simpler and more easy to use: the use scene of recommending group price and automatically multiplexing in batches aiming at the single list is provided, the cloud end is automatically matched according to the list information, and the use is simple and efficient;
5. and visualizing the group price multiplexing result, recording the group price result information (source, accuracy, label, engineering path and the like) of each list, and visually displaying, wherein a user can filter the interface list according to the information to perform classification check.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 specified in the flowchart flow or flows and/or block diagram block or blocks.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (14)
1. A method for rapid pricing in digital building inventory pricing, characterized in that the method handles multiple sources of data, including data collection and data application, wherein,
the data collection comprises data analysis and storage and index establishment, engineering project establishment, data uploading after storage space allocation, big data analysis after analysis of various format data and index database establishment;
the data application comprises single recommendation and batch automatic extraction, the group price data is matched according to the list information, the result after the data application is stored can be stored, and filtering and item-by-item inspection are carried out according to the result.
2. The method for rapid pricing in digital building inventory pricing of claim 1, wherein the data collection is divided into online collection and offline collection based on source.
3. Method for rapid pricing in digital building checklist invoicing according to claim 1 or 2, whereby the data collection comprises in particular:
step 1.1, establishing an engineering project and configuring a storage area;
step 1.2, processing and uploading stored data;
step 1.3, analyzing the multi-source multi-format data;
step 1.4, performing big data analysis by using machine learning;
and step 1.5, indexing the data and establishing an index library.
4. The method for rapid pricing in digital building inventory pricing according to claim 1 or 2, characterized in that the data application comprises:
2.1, selecting a data source to carry out single recommendation;
step 2.2, matching and batch automatic extraction are carried out on the list pricing schemes;
and 2.3, establishing a search system to realize index retrieval and query.
5. The method for rapid pricing in digital building inventory pricing of claim 4, characterized in that after the step 2.1 gets the result, the matched inventory name, characteristics, sub-catalog basic information, work and material machine basic information, labels, data sources and other information are presented.
6. The method of claim 4, wherein the step 2.2 selects the highest matching list pricing scheme each time, saves each pricing result information, provides a subsequent checking interface, and performs data filtering according to the pricing result information.
7. The method of claim 6, wherein the result information of each group price includes data source and matching degree, and the matching degree is standard, similar or null.
8. The method of claim 4, wherein the step 2.3 is to use a machine learning algorithm to normalize the uploaded list name and item feature information based on the knowledge base, perform word segmentation on the name and item feature, search in the inverted index according to the word segmentation result, and output the result with the highest matching degree.
9. A system for rapid pricing in digital building inventory pricing, wherein the system handles a plurality of source numbers, comprising:
the data collection unit is used for analyzing data, warehousing and establishing indexes, establishing engineering projects, uploading data after allocating storage space, analyzing various format data, analyzing big data and establishing an index library;
and the data application unit is used for single recommendation and batch automatic extraction, matching the group price data according to the list information, storing the result after the data application is stored, and filtering and checking item by item according to the result.
10. The system for rapid pricing in digital building inventory pricing of claim 9, wherein the data collection unit separates online collection and offline collection based on source, comprising:
the project building and storing module is used for building engineering projects and configuring a storage area;
the data processing and uploading module is used for processing and uploading the stored data;
the multivariate data analysis module is used for analyzing the multisource and multiformat data;
the big data analysis module is used for carrying out big data analysis by utilizing machine learning;
and the data indexing and indexing module is used for indexing data and establishing an index library.
11. The system for rapid pricing in digital building inventory pricing of claim 9, wherein the data application unit comprises:
the single recommending module is used for selecting a data source to recommend the single item;
the matching and extracting module is used for matching and automatically extracting the list pricing schemes in batches;
and the search query module is used for establishing a search system and realizing index retrieval query.
12. The system for rapid pricing in digital building inventory pricing of claim 11, wherein the single recommending module presents matching inventory names, features, sub-category basic information, work and material machine basic information, tags, data sources and other information after obtaining the results.
13. The system of claim 11, wherein the matching extraction module selects a highest matching listing pricing scheme each time, saves each pricing result information, provides a subsequent check interface, and performs data filtering according to the pricing result information.
14. The system of claim 11, wherein the search query module normalizes the uploaded list name and item feature information based on a knowledge base using a machine learning algorithm, performs word segmentation on the name and item feature, searches in the inverted index according to the word segmentation result, and outputs a result with the highest matching degree.
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