CN115422933B - Cost data extraction method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a cost data extraction method, a cost data extraction device, electronic equipment and a storage medium; the method comprises the following steps: determining the association corresponding relation between the cost subjects and the standard list; acquiring cost data corresponding to the standard list; classifying the cost data into corresponding cost subjects based on the association correspondence; and determining the indexes of the cost data in each cost subject based on index calculation strategies corresponding to each cost subject. Therefore, the cost data can be intelligently extracted, the extraction efficiency of the cost data is improved, and the analysis efficiency of the cost data is improved.
Description
Technical Field
The present invention relates to a cost data extraction technology, and in particular, to a cost data extraction method, a cost data extraction device, an electronic device, and a storage medium.
Background
With the rapid development and wide use of cost data extraction technology, people gradually become the mainstream of cost data extraction technology application by using the cost data extraction technology to extract cost data. However, in the process of extracting cost data, the existing cost data extraction method is to establish a universal cost subject template, manually read the cost data from pricing software according to business requirements, and then manually arrange the cost data in sequence according to cost subjects.
Therefore, it is a constantly pursued goal how to extract the cost data intelligently to improve the extraction efficiency and analysis efficiency of the cost data.
Disclosure of Invention
The embodiment of the invention provides a cost data extraction method and device, electronic equipment and a storage medium.
The invention can solve the technical problems of lower cost data extraction efficiency and lower analysis efficiency in the existing cost data extraction method.
According to a first aspect of the present invention, there is provided a cost data extraction method comprising: determining the association corresponding relation between the cost subjects and the standard list; acquiring cost data corresponding to the standard list; classifying the cost data into corresponding cost subjects based on the association correspondence; and determining the indexes of the cost data in each cost subject based on index calculation strategies corresponding to each cost subject.
According to an embodiment of the present invention, the determining the association relationship between the cost subject and the standard list includes: determining the range of the list features in each cost subject based on the standard information of the cost subjects; determining standard lists corresponding to the cost subjects respectively based on the range of the list characteristics; and determining the association corresponding relation between the cost subject and the standard list.
According to an embodiment of the present invention, the classifying the cost data into the corresponding cost subject based on the association correspondence includes: determining the manifest features in the cost data based on the standard manifest; determining the business classification corresponding to the list features according to a preset business classification phrase matching strategy; determining a corresponding key phrase of the inventory characteristics under the business classification; determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy; and classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation.
According to an embodiment of the present invention, the determining, according to a preset business classification phrase matching policy, a business classification corresponding to the manifest feature includes: determining an attitude key word in the list characteristics and the weight of the attitude key word; responding to the situation that value information corresponding to the business state keywords exists in a preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, and determining a first business state classification corresponding to the business state keywords; and taking the first business state classification as the business state classification corresponding to the list features.
According to an embodiment of the present invention, the determining the cost subject corresponding to the keyword group according to a preset cost subject phrase matching policy includes: determining subject keywords in the keyword group and the weight of the subject keywords; in response to the fact that value information corresponding to the subject keywords exists in a preset cost subject phrase and the weight of the subject keywords meets a preset second weight threshold, determining a first subject classification corresponding to the subject keywords; and taking the first subject classification as the cost subject corresponding to the key phrase.
According to an embodiment of the present invention, the determining the index of the cost data in each cost subject based on the index calculation policy corresponding to each cost subject includes: the index calculation strategy at least comprises: a technical index calculation strategy, an economic index calculation strategy, a unit area cost index calculation strategy and a second-class cost square meter index calculation strategy; determining the technical indexes of the cost data in each cost subject based on the technical index calculation strategy corresponding to each cost subject; determining economic indexes of the cost data in the cost subjects on the basis of the economic index calculation strategies corresponding to the cost subjects respectively; determining a unit area cost index of the cost data in each cost subject based on the unit area cost index calculation strategy corresponding to each cost subject; and determining the square meter index of the second type of expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second type of expenses corresponding to each cost subject.
According to an embodiment of the present invention, before determining the index of the cost data in each cost subject based on the index calculation policy corresponding to each cost subject, the method for extracting the cost data further includes: responding to an index calculation strategy of the cost subject edited by a user to obtain a first index calculation strategy; and storing the first index calculation strategy to the corresponding cost subject.
According to a second aspect of the present invention, there is provided a cost data extraction apparatus comprising: the determining module is used for determining the association corresponding relation between the cost subjects and the standard list; the acquisition module is used for acquiring cost data corresponding to the standard list; the classification module is used for classifying the cost data into corresponding cost subjects based on the association corresponding relation; and the index calculation module is used for determining the indexes of the cost data in each cost subject based on the index calculation strategies corresponding to each cost subject.
According to an embodiment of the present invention, the determining module is configured to: determining the range of listing features in each cost subject based on the standard information of the cost subject; determining a standard list corresponding to each cost subject based on the range of the list characteristics; and determining the association corresponding relation between the cost subject and the standard list.
According to an embodiment of the present invention, the classification module is configured to: determining the manifest features in the cost data based on the standard manifest; determining the business classification corresponding to the list characteristics according to a preset business classification phrase matching strategy; determining a corresponding key phrase of the inventory characteristics under the business classification; determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy; and classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation.
According to an embodiment of the present invention, the classification module is configured to: determining an attitude key word in the list characteristics and the weight of the attitude key word; responding to the situation that value information corresponding to the business state keywords exists in a preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, and determining a first business state classification corresponding to the business state keywords; and taking the first business state classification as the business state classification corresponding to the list features.
According to an embodiment of the present invention, the classification module is configured to: determining subject keywords in the keyword group and the weight of the subject keywords; in response to the fact that value information corresponding to the subject keywords exists in a preset cost subject phrase and the weight of the subject keywords meets a preset second weight threshold, determining a first subject classification corresponding to the subject keywords; and taking the first subject classification as the cost subject corresponding to the key phrase.
According to an embodiment of the present invention, the index calculation strategy at least includes: the system comprises a technical index calculation strategy, an economic index calculation strategy, a unit area cost index calculation strategy and a second-class cost square meter index calculation strategy, wherein the index calculation module is used for: determining the technical indexes of the cost data in each cost subject based on the technical index calculation strategy corresponding to each cost subject; determining economic indexes of the cost data in the cost subjects on the basis of the economic index calculation strategies corresponding to the cost subjects respectively; determining a unit area cost index of the cost data in each cost subject based on the unit area cost index calculation strategy corresponding to each cost subject; and determining the square meter index of the second type of expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second type of expenses corresponding to each cost subject.
According to an embodiment of the present invention, the cost data extracting apparatus further includes: the editing module is used for responding to the index calculation strategy of the cost subject edited by the user to obtain a first index calculation strategy; and the storage module is used for storing the first index calculation strategy to the corresponding cost subject.
According to a third aspect of the present invention, there is provided an electronic apparatus comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the present invention.
According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the present invention.
The method of the embodiment of the invention determines the association corresponding relation between the cost subject and the standard list; acquiring cost data corresponding to the standard list; classifying the cost data into corresponding cost subjects based on the association correspondence; and determining the indexes of the cost data in each cost subject based on index calculation strategies corresponding to each cost subject. Therefore, the cost data can be intelligently extracted, the extraction efficiency of the cost data is improved, and the analysis efficiency of the cost data is improved.
It is to be understood that the teachings of the present invention need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of the present invention may achieve benefits not mentioned above.
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The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description, which proceeds with reference to the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
FIG. 1 is a first schematic process flow diagram illustrating a cost data extraction method according to an embodiment of the present invention;
FIG. 2 is a schematic processing flow diagram of a cost data extraction method according to an embodiment of the present invention;
FIG. 3 is a schematic processing flow diagram III illustrating a cost data extraction method according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a fourth processing flow of the cost data extraction method according to the embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a processing flow of a cost data extraction method according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a sixth processing flow of a cost data extraction method according to an embodiment of the present invention;
FIG. 7 is a process flow diagram seven illustrating a cost data extraction method according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating an application scenario of the cost data extraction method according to an embodiment of the present invention;
FIG. 9 is an alternative diagram of a cost data extraction apparatus provided by an embodiment of the present invention;
fig. 10 is a schematic diagram illustrating a composition structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
In the following description, references to the terms "first", "second", and the like are only to distinguish similar objects and do not denote a particular order, but rather the terms "first", "second", and the like may, where permissible, be interchanged in a particular order or sequence so that embodiments of the invention described herein may be practiced other than as shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
Before further detailed description of the embodiments of the present invention, terms and expressions referred to in the embodiments of the present invention are described, and the terms and expressions referred to in the embodiments of the present invention are applicable to the following explanations.
(1) Cost data: the total currency of the production data value and the worker reward calculated in the estimation, the approximation, the budget, the construction process and the settlement stages of the construction project.
(2) Cost subject: reflecting the cost expense and expenditure, accounting the occurrence and collection condition of the cost and providing accounting subjects of cost-related accounting information.
The existing cost data extraction method is characterized in that a universal cost subject template is established, cost data are manually read from pricing software according to business requirements, and then the cost data are manually arranged in sequence according to cost subjects.
In the related art, in the currently known technical scheme for extracting cost data, because the cost data is manually read from pricing software according to business requirements and then manually arranged in sequence according to cost subjects, the existing process for extracting the cost data is time-consuming and when the cost data is analyzed, the manually arranged cost data is difficult to form a uniform cost caliber. In the related art, time is long in the process of extracting cost data, and the cost data which are manually sorted are difficult to form a uniform cost caliber, so that the problems of low extraction efficiency and low analysis efficiency of the cost data occur.
Aiming at the problems that the time consumption is long in the process of extracting the cost data, the cost data which are manually sorted are difficult to form a uniform cost caliber, and further the extraction efficiency and the analysis efficiency of the cost data are low in the method for extracting the cost data provided by the related technology, the method provided by the embodiment of the invention determines the association corresponding relation between the cost subjects and the standard list; acquiring cost data corresponding to the standard list; classifying the cost data into corresponding cost subjects based on the association corresponding relation; and determining the indexes of the cost data in each cost subject based on index calculation strategies corresponding to each cost subject. Therefore, the cost data can be intelligently and automatically extracted, statistically analyzed, converted and stored, the labor cost is greatly reduced, and the extraction efficiency of the cost data is improved; and establishing a unified cost subject and a standard list, and forming a unified cost caliber of the cost data according to a cost subject set built in the cost management tool. Therefore, compared with the prior art that the time consumption is long in the process of extracting the cost data and the cost data which is manually sorted is difficult to form a uniform cost caliber, the method for extracting the cost data can shorten the time for extracting the cost data, can form the uniform cost caliber of the cost data, and improves the extraction efficiency and the analysis efficiency of the cost data.
A processing flow in the cost data extraction method provided by the embodiment of the present invention is explained. Referring to fig. 1, fig. 1 is a schematic processing flow diagram of a cost data extraction method according to an embodiment of the present invention, which will be described with reference to steps S101 to S104 shown in fig. 1.
And step S101, determining the association corresponding relation between the cost subjects and the standard list.
In some embodiments, the list of criteria may include: manifest name, manifest code, unit, and manifest characteristics.
In some embodiments, step S101 may include: determining the range of the list features in each cost subject based on the standard information of the cost subject; determining standard lists corresponding to the cost subjects respectively based on the range of the list characteristics; and determining the association corresponding relation between the cost subject and the standard list.
In specific implementation, according to the standard information of the main body concrete project of the cost subject A.1.4.1, the list characteristics required to be included in the main body concrete project of the cost subject A.1.4.1 are determined, then according to the list characteristics required to be included in the main body concrete project of the cost subject A.1.4.1, the standard list of the main body concrete project of the cost subject A.1.4.1 is determined, and finally, the association corresponding relationship between the main body concrete project of the cost subject A.1.4.1 and the corresponding standard list is determined. Wherein the manifest features may include: the list under this cost subject relates to the content of correct metering requirements, structural requirements, material requirements and installation methods. The standard list may include: the list name, list code, unit, and list characteristics for all lists under the cost subject.
As an example, the standard information for cost subject a.1.4.1 body concrete projects is shown in table 1 below:
TABLE 1 Standard information of cost subject A.1.4.1 Main body concrete engineering
The cost subject a.1.4.1 is the associated correspondence between the body concrete project and the standard list, as shown in table 2 below:
TABLE 2 Association of cost subject A.1.4.1 Main concrete works with Standard List
And step S102, acquiring cost data corresponding to the standard list.
In some embodiments, the cost data may include: cost data compiled by the user from the standard inventory. The cost data may also include: the system comprises project information, list names, list characteristics, quota extracted from the list and a work machine.
And step S103, classifying the cost data to corresponding cost subjects based on the association corresponding relation.
In some embodiments, step S103 may comprise: determining manifest features in the cost data based on the standard manifest; determining the business classification corresponding to the list characteristics according to a preset business classification phrase matching strategy; determining a corresponding key phrase of the list characteristics under the state classification; determining cost subjects corresponding to the key phrases according to a preset cost subject phrase matching strategy; and classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation.
In some embodiments, the manifest features may include: common list features of standard list settings. The state classification may include: the state of the art in the construction industry. The embodiment of the present invention is not limited to a specific state. The business state is used to represent a form of construction where the use and function in the development of a building is different, such as hotels, apartments, kindergartens, villas, garden houses, pedestrian streets, commercial streets, and department stores. The state classification may also include: a business state, a residential state, and an entertainment state. The key phrases may include: keywords for listing features under the business classification. The keyword sets may include subject keywords.
Aiming at a preset business classification phrase matching strategy, determining a business classification corresponding to the list characteristics, and determining business keywords and the weight of the business keywords in the list characteristics during specific implementation; in response to the fact that value information corresponding to the business state keywords exists in the preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, determining a first business state classification corresponding to the business state keywords; and taking the first business state classification as the business state classification corresponding to the list characteristics.
In some embodiments, the state keywords may include: the embodiment of the present invention does not limit specific keywords corresponding to the business state. The preset business classification phrases may include: and keywords corresponding to all the business categories in the preset construction industry. The weight of the state keyword may include: the ratio of the number of list items with the appearance of the modal keywords in the list characteristics to the number of all list characteristic items; the larger the ratio is, the larger the weight corresponding to the ratio is, and the invention does not limit the weight of the specific business state keyword. The preset first weight threshold may include: and the preset minimum weight capable of determining the first state classification corresponding to the state key words. The first business state classification may include: and the business classification corresponding to the business keyword with the maximum weight.
In some embodiments, by using a semantic analysis technique, determining an industry status keyword and a weight corresponding to the industry status keyword in the inventory feature of the cost data, determining whether value information corresponding to the industry status keyword exists in a preset industry status classification phrase, determining a first industry status classification corresponding to the industry status keyword with the highest weight when the value information corresponding to the industry status keyword exists in the preset industry status classification phrase and the weight of the industry status keyword is greater than or equal to a preset first weight threshold, and using the first industry status classification as the industry status classification corresponding to the inventory feature.
As an example, the first weight threshold of the business-state keyword "cell" is 0.2, the list features of the cost data 1 are structured by a word formation and word segmentation technology, the weight 0.5 corresponding to the "cell" and the "cell" is determined, and whether value information corresponding to the "cell" exists in a preset business-state classification phrase is determined. If the preset business state classification phrase has value information corresponding to the "cell" and the weight 0.5 of the "cell" is greater than the preset first weight threshold 0.2, it is determined that the business state corresponding to the "cell" is classified as the home business state, and the home business state is classified as the business state of the list feature of the cost data 1.
Determining cost subjects corresponding to the key phrases according to a preset cost subject phrase matching strategy, and determining subject keywords in the key phrases and weights of the subject keywords in the key phrases during specific implementation; determining a first subject classification corresponding to the subject keywords in response to the fact that the value information of the corresponding subject keywords exists in the preset cost subject phrase and the weight of the subject keywords meets a preset second weight threshold; and taking the first subject classification as the cost subject corresponding to the key phrase.
In some embodiments, the subject keywords may include: the embodiment of the present invention does not limit the specific keywords corresponding to the cost subject. The preset cost subject phrase may include: keywords corresponding to all cost subjects under preset business classification. The weight of the subject keyword may include: the ratio of the number of list items with subject keywords in the list features to the number of all list feature items is larger, the weight corresponding to the ratio is larger, and the invention does not limit the weight of specific subject keywords. The preset second weight threshold may include: and the preset minimum weight can determine the first subject classification corresponding to the subject key words. The first subject classification may include: and classifying the subjects corresponding to the subject keywords with the largest weight.
In some embodiments, the subject keywords in the keyword group and the weights corresponding to the subject keywords are determined through a semantic analysis technology, whether value information corresponding to the subject keywords exists in a preset cost subject phrase or not is judged, when the value information corresponding to the subject keywords exists in the preset cost subject phrase and the weights of the subject keywords are greater than or equal to a preset second weight threshold value, a first subject classification corresponding to the subject keyword with the largest weight is determined, and the first subject classification is used as the cost subject corresponding to the list feature.
As an example, the second weight threshold of the subject keyword "concrete project" is 0.3, the keyword group 1 is structured by a word formation and word segmentation technology, the weight 0.5 corresponding to the "concrete project" and the "concrete project" is determined, and whether value information corresponding to the "concrete project" exists in the preset cost subject phrase is determined. And if the preset state classification phrase has value information corresponding to the concrete project and the weight 0.5 of the concrete project is greater than the preset second weight threshold value 0.3, determining that the cost subject corresponding to the concrete project is classified as a main concrete project and taking the main concrete project as the cost subject of the key phrase 1.
And step S104, determining indexes of cost data in each cost subject based on index calculation strategies corresponding to each cost subject.
In some embodiments, before step S104, the cost data extracting method may further include: responding to an index calculation strategy edited by a user to obtain a first index calculation strategy; and storing the first index calculation strategy to the corresponding cost subject.
In specific implementation, a user can edit the index calculation strategy of each cost subject, respond to the index calculation strategy of the cost subject edited by the user, use the index calculation strategy edited by the user as a first index calculation strategy, and store the first index calculation strategy to the corresponding cost subject. Based on the first index calculation strategy, a first index of cost data in the cost subject corresponding to the first index calculation strategy can be determined. The first index may include: and the index is obtained by calculating the cost data according to the first index calculation strategy and is preset by the user. The first index may include: technical indexes, economic indexes, unit area cost indexes and second-class cost square meter indexes.
In some embodiments, the metric calculation strategy may include: a technical index calculation strategy, an economic index calculation strategy, a unit area cost index calculation strategy and a second-class cost square meter index calculation strategy.
In the specific implementation, the technical index a for determining the cost data in each cost subject can be represented by the following formula (1):
wherein m represents the sum of the engineering quantities of all standard lists under the cost subject in the same business state, and n represents the building area in the business state.
Aiming at the economic index calculation strategy respectively corresponding to each cost subject, determining the economic index of the cost data in each cost subject, and in specific implementation, determining the economic index B of the cost data in each cost subject can be represented by the following formula (2):
wherein h represents the total price of the standard list under the cost subject in the same state, n represents the building area in the state, and A represents the technical index of the cost data in the cost subject.
Aiming at a unit area cost index calculation strategy respectively corresponding to each cost subject, determining a unit area cost index of cost data in each cost subject, and when the method is implemented specifically, determining a unit area cost index C of the cost data in each cost subject can be represented by the following formula (3):
wherein i represents the total price of the standard list in the state, and n represents the building area in the state.
For the square meter index calculation strategy based on the second type of expenses corresponding to each cost subject, the square meter index of the second type of expenses of the cost data in each cost subject is determined, and in specific implementation, the square meter index D of the second type of expenses of the cost data in each cost subject can be represented by the following formula (4):
where k represents the sum of all contract prices in the cost subject, and j represents the total building area of the project.
In some embodiments, a processing flow diagram of the cost data extraction method shown in fig. 2 includes:
step S201, determining a range of the listing feature in each cost subject based on the standard information of the cost subject.
Step S202, based on the range of the list features, determining a standard list corresponding to each cost subject.
Step S203, determining the association corresponding relation between the cost subject and the standard list.
For the specific description of the steps S201 to S203, which is first to determine the range of the list feature based on the standard information of the cost subject, then to determine the standard list based on the range of the list feature, and finally to determine the association correspondence between the cost subject and the standard list, the same as the step S101 is performed, and details are not repeated here.
After step S203, the cost data extraction method further includes: and establishing a cost subject system according to the association corresponding relation. Wherein, the cost subject system may include: the system comprises a cost subject code, a cost subject name, a level, a financial aperture attribution, a compiling description, a calculating aperture, a technical index statistical unit, a technical index calculating strategy, an economic index calculating strategy, a unit area cost index calculating strategy, a square meter index calculating strategy of second-class expense and a corresponding relation between the cost subject and a standard list.
In some embodiments, a processing flow diagram of the cost data extraction method shown in fig. 3 includes:
step S301 determines a manifest feature in the cost data based on the standard manifest.
Step S302, according to a preset business classification phrase matching strategy, business classification corresponding to the list characteristics is determined.
Step S303, determining a keyword group corresponding to the list feature under the state classification.
For steps S301 to S303, in specific implementation, first, the list feature in the cost data is determined based on the common list feature set by the standard list. And then, according to a preset business classification phrase matching strategy, performing business keyword extraction and matching on the list features, and determining the business classification corresponding to the list features. And finally, determining a keyword group of the list characteristics under the state classification, wherein the keyword group can comprise subject keywords.
And step S304, determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy.
Step S305, based on the association corresponding relationship, classifying the cost data into the cost subject corresponding to the key phrase.
For steps S304 and S305, in a specific implementation, firstly, according to a preset cost subject phrase matching policy, subject keyword extraction and matching are performed on a keyword phrase, and a cost subject corresponding to the keyword phrase is determined. And then, classifying the cost data into cost subjects corresponding to the key phrases according to the cost subjects corresponding to the key phrases.
The detailed descriptions of the listing features, the business classification and the keyword sets in steps S301 to S305 are the same as those in step S103, and are not repeated here.
In some embodiments, a processing flow diagram of the cost data extraction method is four, as shown in fig. 4, and includes:
step S401, determining the status keywords and the weights of the status keywords in the list features.
Step S402, in response to the preset business state classification phrase having the value information corresponding to the business state keyword and the weight of the business state keyword satisfying the preset first weight threshold, determining a first business state classification corresponding to the business state keyword.
In step S403, the first format classification is used as the format classification corresponding to the list feature.
The detailed descriptions of the business state keywords, the preset business state classification phrases, the weights of the business state keywords, the preset first weight threshold, and the first business state classification in steps S401 to S403 are the same as those in step S103, and are not repeated here.
As an example, for steps S401-S403, in concrete implementation, the first weight threshold of the business-state keyword "cell" is 0.2; the first weight threshold value of the business-state keyword 'hotel' is 0.3, the list features of the cost data 1 are structured through a word construction and word segmentation technology, the weight 0.7 corresponding to the 'cell' and the 'cell' is determined, and the weight 0.4 corresponding to the 'hotel' and the 'hotel' is determined. And judging whether value information corresponding to the 'cell' and the 'hotel' exists in a preset business classification phrase. The value information corresponding to the 'cell' exists in the preset business state classification phrase, the weight 0.7 of the 'cell' is larger than a preset first weight threshold value 0.2, the value information corresponding to the 'hotel' exists in the preset business state classification phrase, the weight 0.4 of the 'hotel' is larger than a preset first weight threshold value 0.3, the weight 0.7 of the 'cell' is larger than the weight 0.4 of the 'hotel', the 'cell' is determined to be a business state keyword with the largest weight, finally, the business state corresponding to the 'cell' is determined to be the home business state, and the home business state is used as the business state classification of the list feature of the cost data 1.
In some embodiments, a schematic processing flow diagram of the cost data extraction method is five, as shown in fig. 5, and includes:
step S501, determining subject keywords in the keyword group and weights of the subject keywords.
Step S502, in response to that the value information of the corresponding subject keyword exists in the preset cost subject phrase and the weight of the subject keyword satisfies a preset second weight threshold, determining a first subject classification corresponding to the subject keyword.
Step S503, using the first subject classification as the cost subject corresponding to the keyword group.
The specific descriptions of the subject keywords, the preset cost subject phrases, the weights of the subject keywords, the preset second weight threshold and the first subject classification in steps S501 to S503 are the same as those in step S103, and are not repeated here.
As an example, for steps S501-S503, in concrete implementation, the second weight threshold of the subject keyword "concrete engineering" is 0.3; the second weight threshold of the subject keyword 'steel bar engineering' is 0.4, the keyword group 1 is structured through a word formation and word segmentation technology, the weight 0.5 corresponding to the 'concrete engineering' and the 'concrete engineering' is determined, the weight 0.3 corresponding to the 'steel bar engineering' and the 'steel bar engineering' is determined, and whether the preset cost subject phrase has value information corresponding to the 'concrete engineering' and the 'steel bar engineering' is judged. The preset business classification phrase has value information corresponding to the concrete project, and the weight 0.5 of the concrete project is greater than a preset second weight threshold value 0.3; if value information corresponding to the steel bar project exists in a preset condition classification phrase, but the weight 0.3 of the steel bar project is smaller than a preset second weight threshold value 0.4, determining that the cost subject corresponding to the concrete project is classified as a main concrete project, and taking the main concrete project as the cost subject of the key phrase 1.
In some embodiments, a schematic processing flow diagram of the cost data extraction method is shown in fig. 6, and includes:
step S601, determining the technical index of the cost data in each cost subject based on the technical index calculation policy corresponding to each cost subject.
Step S602, determining economic indexes of the cost data in each cost subject based on economic index calculation strategies corresponding to each cost subject.
And step S603, determining the unit area cost index of the cost data in each cost subject based on the unit area cost index calculation strategy respectively corresponding to each cost subject.
Step S604, determining the square meter indexes of the second type expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second type expenses corresponding to each cost subject.
Specific descriptions of determining the indexes of the cost data in each cost subject based on the index calculation strategies respectively corresponding to each cost subject in steps S601 to S604 are the same as those in step S104, and are not described herein again.
In some embodiments, a process flow diagram of the cost data extraction method is shown as a seventh, as shown in fig. 7, and includes:
step S701, responding to the index calculation strategy edited by the user to obtain a first index calculation strategy.
Step S702, storing the first index calculation strategy to the corresponding cost subject.
As an example, for steps S701 to S702, in a specific implementation, the user edits the technical index calculation policy of cost subject 1, and in response to the user editing the technical index calculation policy, the technical index calculation policy edited by the user is used as a first index calculation policy, and the first index calculation policy is stored in cost subject 1. Based on the first index calculation strategy, the technical index of cost subject 1 may be determined.
Fig. 8 is an application scenario diagram illustrating a cost data extraction method according to an embodiment of the present invention.
Referring to fig. 8, an application scenario of the cost data extraction method according to the embodiment of the present invention is applied to standardized extraction of cost data. For step S1, firstly, according to the standard information of the cost subject, determining the list features required to be included in the cost subject, then, according to the list features required to be included in the cost subject, determining the standard list of the cost subject, and finally, determining the association correspondence between the cost subject and the corresponding standard list. And establishing a cost subject system according to the association corresponding relation. The cost subject system may include: the system comprises a cost subject code, a cost subject name, a level, a financial aperture attribution, a compiling description, a calculating aperture, a technical index statistical unit, a technical index calculating strategy, an economic index calculating strategy, a unit area cost index calculating strategy, a square meter index calculating strategy of the second type of cost and a corresponding relation between the cost subject and a standard list.
As an example, a cost subject system for real estate development project costs can include: the real estate development project cost = A project expense + B project construction other expense + C preparation expense + D construction period interest + E bottom-laying liquidity fund; wherein, the project cost A = A.1 monomer project cost + A.2 outdoor project cost + A.3 measure cost + A.4 other project cost + A.5 standard fee and tax; wherein, the A.1 monomer engineering cost = A.1.1 foundation pit earthwork + A.1.2 foundation treatment and pile foundation engineering + A.1.3 side slope support and drainage + A.1.4 construction engineering + A.1.5 decoration engineering (initial assembly and repair) + other monomer engineering; wherein, A.1.4 building engineering = A.1.4.1 main body concrete engineering + A.1.4.2 main body steel bar engineering + A.1.4.3 steel structure engineering + A.1.4.4 precast concrete member engineering.
The cost subject system is built in a cost management tool of the pricing software, and a user can compile cost files of each stage of estimation, approximate calculation, budget, construction process and settlement according to a standard list in the pricing software.
And aiming at the step S2, extracting the cost data in the cost file. Wherein the cost data may include: the system comprises project information, list names, list characteristics, quota extracted from the list and a work machine.
For step S3, firstly, the list features in the cost data are determined according to the common list features set by the standard list. And determining the business classification corresponding to the list characteristics according to a preset business classification phrase matching strategy. And determining the corresponding key phrase of the list characteristics under the state classification. And determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy. And finally, classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation.
And aiming at the step S4, determining the technical index, the economic index, the unit area cost index and the square meter index of the second type of cost of the cost data in each cost subject based on the technical index calculation strategy, the economic index calculation strategy, the unit area cost index calculation strategy and the square meter index calculation strategy of the second type of cost which are respectively corresponding to each cost subject.
For step S5, cost data and respective indices of the cost data in the respective cost subjects are stored.
It is understood that the application scenario of the cost data extraction method in fig. 8 is only a partial exemplary implementation manner in the embodiment of the present invention, and the application scenario of the cost data extraction method in the embodiment of the present invention includes, but is not limited to, the application scenario of the cost data extraction method shown in fig. 8.
The method of the embodiment of the invention determines the range of the list characteristics in each cost subject based on the standard information of the cost subject; determining standard lists corresponding to the cost subjects respectively based on the range of the list features; and determining the association corresponding relation between the cost subject and the standard list, so that a uniform cost subject system can be established, the cost subject system is built in the cost management tool and establishes the association corresponding relation with the standard list in the software, a uniform cost caliber is formed, the extraction efficiency of the cost data is improved, and the analysis efficiency of the cost data is improved. The method of the embodiment of the invention determines the list characteristics in the cost data based on the standard list; determining the business classification corresponding to the list characteristics according to a preset business classification phrase matching strategy; and determining the corresponding key phrases of the list features under the state classification, so that the tedious work of manually processing cost data can be avoided, all cost data related to the state are automatically extracted from the engineering file and calculated and summarized, and the extraction efficiency of the cost data and the analysis efficiency of the cost data are improved. According to the method provided by the embodiment of the invention, the cost subject corresponding to the key phrase is determined according to a preset cost subject phrase matching strategy; based on the association corresponding relation, the cost data are classified to the cost subjects corresponding to the key word groups, so that the cost data can be automatically classified to the respective cost subjects, automatic identification and collection are realized, the extraction efficiency of the cost data is improved, and the analysis efficiency of the cost data is improved. The method of the embodiment of the invention determines the index of the cost data in each cost subject based on the index calculation strategy respectively corresponding to each cost subject, so that the cost data can be automatically converted into the index according to the standardized cost caliber and calculation rule, the index is consistent with the cost caliber of the cost data, a foundation is laid for the transverse and longitudinal comparison analysis of the subsequent cost data, the extraction efficiency of the cost data is improved, and the analysis efficiency of the cost data is improved.
Therefore, compared with the prior art that the time consumption is long in the process of extracting the cost data and the cost data which is manually sorted is difficult to form a uniform cost caliber, the cost data extracting method can shorten the time for extracting the cost data, can form the uniform cost caliber of the cost data, and improves the extracting efficiency and the analyzing efficiency of the cost data.
Continuing with the exemplary structure of the cost data extraction device 90 provided by the embodiment of the present invention implemented as software modules, in some embodiments, as shown in fig. 9, the software modules in the cost data extraction device 90 may include: a determining module 901, configured to determine an association correspondence between a cost subject and a standard list; an obtaining module 902, configured to obtain cost data corresponding to the standard manifest; a classification module 903, configured to classify the cost data into corresponding cost subjects based on the association correspondence; and the index calculation module 904 is configured to determine an index of the cost data in each cost subject based on an index calculation policy corresponding to each cost subject.
In some embodiments, the determining module 901 is specifically configured to, in the process of determining the association correspondence between the cost subject and the standard list: determining the range of the list features in each cost subject based on the standard information of the cost subject; determining standard lists corresponding to the cost subjects respectively based on the range of the list features; and determining the association corresponding relation between the cost subject and the standard list.
In some embodiments, the classifying module 903 is specifically configured to, in the process of classifying the cost data into the corresponding cost subject based on the association correspondence relationship: determining manifest features in the cost data based on the standard manifest; determining the business classification corresponding to the list characteristics according to a preset business classification phrase matching strategy; determining a corresponding key phrase of the list characteristics under the state classification; determining cost subjects corresponding to the key phrases according to a preset cost subject phrase matching strategy; and classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation.
In some embodiments, the classifying module 903, in the process of determining the business classification corresponding to the manifest feature according to a preset business classification phrase matching policy, is specifically configured to: determining the state keywords in the list characteristics and the weight of the state keywords; in response to the fact that value information corresponding to the business state keywords exists in a preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, determining a first business state classification corresponding to the business state keywords; and taking the first business state classification as the business state classification corresponding to the list features.
In some embodiments, the classifying module 903 is specifically configured to, in the process of determining the cost subject corresponding to the keyword group according to a preset cost subject phrase matching policy: determining subject keywords in the keyword group and the weights of the subject keywords; determining a first subject classification corresponding to the subject keywords in response to the fact that the value information of the corresponding subject keywords exists in the preset cost subject phrase and the weight of the subject keywords meets a preset second weight threshold; and taking the first subject classification as the cost subject corresponding to the key phrase.
In some embodiments, the index calculating module 904, in the process of determining the index of the cost data in each cost subject based on the index calculating policy corresponding to each cost subject, is specifically configured to: determining the technical indexes of the cost data in each cost subject based on the technical index calculation strategy corresponding to each cost subject; determining economic indexes of cost data in each cost subject based on economic index calculation strategies corresponding to each cost subject; determining unit area cost indexes of the cost data in each cost subject based on unit area cost index calculation strategies corresponding to each cost subject; and determining the square meter indexes of the second-class expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second-class expenses respectively corresponding to each cost subject.
In some embodiments, the cost data extracting device 90 may further include: the editing module 905 is used for responding to the index calculation strategy edited by the user for the cost subject to obtain a first index calculation strategy; a storage module 906, configured to store the first index calculation policy to the corresponding cost subject.
It should be noted that the description of the apparatus according to the embodiment of the present invention is similar to the description of the method embodiment, and has similar beneficial effects to the method embodiment, and therefore, the description is omitted. The inexhaustible technical details in the cost data extraction device provided by the embodiment of the invention can be understood according to the description of any one of the drawings in fig. 1 to 9.
The invention also provides an electronic device and a non-transitory computer readable storage medium according to an embodiment of the invention.
FIG. 10 shows a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 10, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic apparatus 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server combining a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (12)
1. A cost data extraction method, characterized in that the cost data extraction method comprises:
determining the association corresponding relation between the cost subjects and the standard list;
acquiring cost data corresponding to the standard list;
determining manifest features in the cost data based on the standard manifest;
determining an attitude keyword in the list characteristics and the weight of the attitude keyword;
responding to the situation that value information corresponding to the business state keywords exists in a preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, and determining a first business state classification corresponding to the business state keywords;
taking the first business state classification as a business state classification corresponding to the list features;
determining a corresponding key phrase of the inventory characteristics under the business classification;
determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy;
classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation;
and determining the indexes of the cost data in each cost subject based on index calculation strategies corresponding to each cost subject.
2. The method of claim 1, wherein determining the associative correspondence between the cost items and the standard list comprises:
determining a range of the listing features in each of the cost subjects based on standard information of the cost subjects;
determining standard lists corresponding to the cost subjects respectively based on the range of the list characteristics;
and determining the association corresponding relation between the cost subject and the standard list.
3. The method according to claim 2, wherein the determining the cost subject corresponding to the keyword group according to a preset cost subject phrase matching policy comprises:
determining subject keywords in the keyword group and the weight of the subject keywords;
determining a first subject classification corresponding to the subject keyword in response to the fact that value information corresponding to the subject keyword exists in a preset cost subject phrase and the weight of the subject keyword meets a preset second weight threshold;
and taking the first subject classification as the cost subject corresponding to the key phrase.
4. The method of claim 1, wherein determining the index of the cost data in each of the cost subjects based on the index calculation policy corresponding to each of the cost subjects comprises:
the index calculation strategy at least comprises: a technical index calculation strategy, an economic index calculation strategy, a unit area cost index calculation strategy and a second-class cost square meter index calculation strategy;
determining the technical indexes of the cost data in each cost subject based on the technical index calculation strategy corresponding to each cost subject;
determining economic indexes of the cost data in each cost subject based on the economic index calculation strategy corresponding to each cost subject;
determining a unit area cost index of the cost data in each cost subject based on the unit area cost index calculation strategy corresponding to each cost subject;
and determining the square meter index of the second type of expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second type of expenses corresponding to each cost subject.
5. The method according to claim 1, wherein before determining the index of the cost data in each of the cost subjects based on the index calculation policy corresponding to each of the cost subjects, the method for extracting the cost data further comprises:
responding to an index calculation strategy of the cost subject edited by a user to obtain a first index calculation strategy;
and storing the first index calculation strategy to the corresponding cost subject.
6. A cost data extraction device, characterized in that the cost data extraction device comprises:
the determining module is used for determining the association corresponding relation between the cost subject and the standard list;
the acquisition module is used for acquiring cost data corresponding to the standard list;
a classification module for determining inventory characteristics in the cost data based on the standard inventory; determining an attitude key word in the list characteristics and the weight of the attitude key word; responding to the situation that value information corresponding to the business state keywords exists in a preset business state classification phrase and the weight of the business state keywords meets a preset first weight threshold value, and determining a first business state classification corresponding to the business state keywords; taking the first business state classification as a business state classification corresponding to the list features; determining a corresponding key phrase of the inventory characteristics under the business classification; determining the cost subject corresponding to the key phrase according to a preset cost subject phrase matching strategy; classifying the cost data into cost subjects corresponding to the key phrases based on the association corresponding relation;
and the index calculation module is used for determining the indexes of the cost data in each cost subject based on the index calculation strategies corresponding to each cost subject.
7. The apparatus of claim 6, wherein the determining module is configured to:
determining a range of the listing features in each of the cost subjects based on the standard information of the cost subjects;
determining a standard list corresponding to each cost subject based on the range of the list characteristics;
and determining the association corresponding relation between the cost subject and the standard list.
8. The apparatus of claim 7, wherein the classification module is configured to:
determining subject keywords in the keyword group and the weight of the subject keywords;
in response to the fact that value information corresponding to the subject keywords exists in a preset cost subject phrase and the weight of the subject keywords meets a preset second weight threshold, determining a first subject classification corresponding to the subject keywords;
and taking the first subject classification as the cost subject corresponding to the key phrase.
9. The apparatus of claim 6, wherein the metric calculation strategy comprises at least: the system comprises a technical index calculation strategy, an economic index calculation strategy, a unit area cost index calculation strategy and a second-class cost square meter index calculation strategy, wherein the index calculation module is used for:
determining the technical indexes of the cost data in each cost subject based on the technical index calculation strategy corresponding to each cost subject;
determining economic indexes of the cost data in each cost subject based on the economic index calculation strategy corresponding to each cost subject;
determining a unit area cost index of the cost data in each cost subject based on the unit area cost index calculation strategy corresponding to each cost subject;
and determining the square meter index of the second type of expenses of the cost data in each cost subject based on the square meter index calculation strategy of the second type of expenses corresponding to each cost subject.
10. The apparatus of claim 6, wherein the cost data extraction means further comprises:
the editing module is used for responding to the index calculation strategy of the cost subject edited by the user to obtain a first index calculation strategy;
and the storage module is used for storing the first index calculation strategy to the corresponding cost subject.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
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