CN116188091A - Method, device, equipment and medium for automatic matching unit price reference of cost list - Google Patents
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Abstract
The application discloses a method, a device, equipment and a medium for automatically matching unit price quotation in a cost list, wherein the method comprises the following steps: acquiring first list data and second list data in an engineering supervision database; performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data; inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model; and acquiring an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model. The method and the device can replace manual input of a user, automatically synchronize unit prices of the plurality of cost lists, automatically correlate unit price relations, improve unit price quotation efficiency and accuracy of the cost lists, and reduce labor cost and time cost.
Description
Technical Field
The application relates to the technical field of engineering cost artificial intelligence, in particular to a method, a device, equipment and a medium for automatically matching unit price quotation of a cost list.
Background
In the process of construction cost, the construction cost list refers to the project quantity list of bidding projects provided by a bidding person in a bidding document when construction bidding is established, and the bidding person performs a price-calculating action independently quoted by the bidding person, and the data of the bid price is taken as the data of the bid price of the project after the total is carried out according to the absolute values calculated by the project quantity list and the comprehensive unit price of the listed projects, namely, the labor cost, the material cost, the indirect cost and the like, and is mainly applied to bidding and is also applicable to project approximation and pre-settlement.
In the related art, after the project file is imported, a user usually needs to manually and manually conduct unit price quotation on the list to achieve price synchronization of a plurality of project cost lists, and the manual unit price quotation mode not only increases labor cost and time cost, but also causes manual operation errors, so that unit price quotation efficiency and accuracy of the project cost list are lower.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for automatically matching unit price quotation of a cost list, which can replace manual input of a user, automatically synchronize unit price of a plurality of cost lists and automatically correlate unit price relations, improve unit price quotation efficiency and accuracy of the cost list and reduce labor cost and time cost.
The embodiment of the application provides a method for automatically matching unit price references in a cost list, which comprises the following steps:
acquiring first list data and second list data in an engineering supervision database;
performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data;
inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model;
and acquiring an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
In some embodiments, the training manner of the logistic regression model includes:
acquiring first preset list data and second preset list data in an engineering supervision database;
respectively carrying out data cleaning and data preprocessing on the first preset list data and the second preset list data to obtain a first preset vector and a second preset vector after cleaning and preprocessing;
inputting the first preset vector as a first training sample into an initial logistic regression model, and inputting the second preset vector as a second training sample into the initial logistic regression model;
acquiring a training analysis result of whether a reference relationship exists between the first preset list data and the second preset list data output by the initial logistic regression model;
when the similarity between the training analysis result and the preset analysis result is greater than or equal to a preset threshold value, the initial logistic regression model is successfully trained to obtain the pre-trained logistic regression model;
and when the similarity between the training analysis result and the preset analysis result is smaller than a preset threshold value, continuing training the initial logistic regression model by adjusting parameters in the initial logistic regression model until the initial logistic regression model is successfully trained.
In some embodiments, performing data cleaning and data preprocessing on the first preset list data and the second preset list data respectively to obtain a first preset vector and a second preset vector after cleaning and preprocessing, including:
performing data cleaning on the first preset list data to obtain first preset clean data, and performing One-Hot independent encoding on the first preset clean data to obtain a first preset vector;
and performing data cleaning on the second preset list data to obtain second preset clean data, and performing One-Hot independent encoding on the second preset clean data to obtain a second preset vector.
In some embodiments, the method comprises:
dividing a plurality of list item features in the first preset list data or the second preset list data into a plurality of feature major classes according to at least two dimensions, wherein each feature major class comprises a plurality of different feature minor classes;
performing One-Hot independent coding treatment on the feature major class and the feature minor class respectively;
combining the One-Hot single-heat coding processing results of the feature major class and the feature minor class to obtain a first vector; wherein the first vector is a two-dimensional vector (feature big class, feature small class).
In some embodiments, the method further comprises:
performing word segmentation on the list names in the first preset list data or the second preset list data respectively to obtain word segmentation results;
performing One-Hot independent encoding treatment on the word segmentation result to obtain a second vector; wherein the second vector has a vector length of 20.
In some embodiments, the method further comprises:
performing One-Hot independent encoding processing on list units in the first preset list data or the second preset list data to obtain a third vector; wherein the third vector is a one-dimensional vector;
the first, second and third vectors are combined to obtain a combined vector (20,2,1).
In some embodiments, inputting the first preset vector as a first training sample into an initial logistic regression model and inputting the second preset vector as a second training sample into an initial logistic regression model comprises:
taking the combined vector (20,2,1) of the first preset list data or the second preset list data as a first preset vector and a second preset vector respectively;
the combined vector (20,2,1) of the first preset inventory data is input as a first training sample into an initial logistic regression model, and the combined vector (20,2,1) of the second preset inventory data is input as a second training sample into an initial logistic regression model.
The embodiment of the application also provides a device for automatically matching the unit price reference with the cost list, which comprises:
the data acquisition unit is configured to acquire first list data and second list data in the engineering supervision database;
the data processing unit is configured to perform data preprocessing on the first list data to obtain first preprocessed data, and perform data preprocessing on the second list data to obtain second preprocessed data;
a data input unit configured to input the first pre-processed data and the second pre-processed data to a pre-trained logistic regression model;
and the analysis result acquisition unit is configured to acquire an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
The embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method for automatically matching a monovalent reference to a cost list.
The embodiment of the application also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of being run in the processor, wherein the method for automatically matching the unit price reference with the cost list is realized when the processor executes the computer program.
The embodiment of the application adopts the following technical scheme:
acquiring first list data and second list data in an engineering supervision database; performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data; inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model; and acquiring an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
the method can replace manual input of a user, automatically synchronize unit prices of a plurality of cost lists and automatically correlate unit price relations, improve unit price quotation efficiency and accuracy of the cost lists and reduce labor cost and time cost.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a method for automatically matching a price list with a unit price reference according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a training process of a logistic regression model according to an embodiment of the present application;
FIG. 3 is a schematic diagram of performing data cleaning and One-Hot independent encoding on inventory data according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an application of a logistic regression model according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a device for automatically matching a price list with a unit price reference according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a flow chart of a method for automatically matching a unit price reference to a price list according to an embodiment of the present application, where the method for automatically matching a unit price reference to a price list according to an embodiment of the present application includes the following steps:
s101, acquiring first list data and second list data in an engineering supervision database.
S102, performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data.
S103, inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model.
S104, obtaining an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
In some embodiments, the training manner of the logistic regression model according to the embodiments of the present application includes:
acquiring first preset list data and second preset list data in an engineering supervision database;
respectively performing data cleaning and data preprocessing on the first preset list data and the second preset list data to obtain a first preset vector and a second preset vector after cleaning and preprocessing;
inputting a first preset vector as a first training sample into an initial logistic regression model, and inputting a second preset vector as a second training sample into the initial logistic regression model;
acquiring a training analysis result of whether a reference relationship exists between first preset list data and second preset list data output by an initial logistic regression model;
when the similarity between the training analysis result and the preset analysis result is greater than or equal to a preset threshold value, the initial logistic regression model is successfully trained, and a pre-trained logistic regression model is obtained;
when the similarity between the training analysis result and the preset analysis result is smaller than the preset threshold value, the initial logistic regression model is continuously trained by adjusting parameters in the initial logistic regression model until the initial logistic regression model is successfully trained.
It should be noted that, in the embodiment of the present application, the preset threshold is used to indicate the similarity between the training analysis result and the preset analysis result, and the user may set the similarity according to the actual service requirement, which is not limited in the embodiment of the present application.
In some embodiments, performing data cleaning and data preprocessing on the first preset list data and the second preset list data respectively to obtain a first preset vector and a second preset vector after cleaning and preprocessing, including:
performing data cleaning on the first preset list data to obtain first preset clean data, and performing One-Hot independent encoding on the first preset clean data to obtain a first preset vector;
and performing data cleaning on the second preset list data to obtain second preset clean data, and performing One-Hot independent encoding on the second preset clean data to obtain a second preset vector.
In some embodiments, the method of automatically matching a monovalent reference to a bill of manufacturing costs includes:
dividing a plurality of list item features in the first preset list data or the second preset list data into a plurality of feature major classes according to at least two dimensions, wherein each feature major class comprises a plurality of different feature minor classes;
performing One-Hot independent encoding treatment on the feature major class and the feature minor class respectively;
combining the One-Hot single-heat coding processing results of the feature major class and the feature minor class to obtain a first vector; wherein the first vector is a two-dimensional vector (feature big class, feature small class).
In some embodiments, the method of automatically matching a monovalent reference to a bill of manufacturing costs further comprises:
respectively performing word segmentation on the list names in the first preset list data or the second preset list data to obtain word segmentation results;
performing One-Hot independent encoding treatment on the word segmentation result to obtain a second vector; wherein the second vector has a vector length of 20.
In some embodiments, the method of automatically matching a monovalent reference to a bill of manufacturing costs further comprises:
performing One-Hot independent encoding processing on list units in the first preset list data or the second preset list data to obtain a third vector; wherein the third vector is a one-dimensional vector;
the first vector, the second vector and the third vector are combined to obtain a combined vector (20,2,1).
In some embodiments, inputting the first preset vector as a first training sample into the initial logistic regression model and inputting the second preset vector as a second training sample into the initial logistic regression model includes:
taking the combined vector (20,2,1) of the first preset list data or the second preset list data as a first preset vector and a second preset vector respectively;
the combined vector (20,2,1) of the first preset inventory data is input as a first training sample to the initial logistic regression model, and the combined vector (20,2,1) of the second preset inventory data is input as a second training sample to the initial logistic regression model.
As shown in fig. 2, fig. 2 is a schematic diagram of a training process of the logistic regression model provided in the embodiment of the present application, where the known list in fig. 2 is first preset list data, the known list in second preset list data is second preset list data, and the first preset list data and the second preset list data are respectively subjected to data cleaning to obtain clean first preset clean data and clean second preset clean data. And performing One-Hot single-heat encoding processing on a plurality of list item features, list names and list units in the first preset clean data and the second preset clean data respectively to obtain a first vector (vector length is 2), a second vector (vector length is 20) and a third vector (vector length is 1), combining the first vector, the second vector and the third vector into a combined vector (20,2,1), and expressing the combined vector as (list names, list item features and list units).
The combined vector (20,2,1) of the first preset inventory data is input as a first training sample to the initial logistic regression model, and the combined vector (20,2,1) of the second preset inventory data is input as a second training sample to the initial logistic regression model. And inputting a preset analysis result of whether a reference relation exists between the first preset list data and the second preset list data into the initial logistic regression model. And acquiring a training analysis result of whether a reference relationship exists between the first preset list data and the second preset list data output by the initial logistic regression model.
Comparing the training analysis result with a preset analysis result, and when the similarity between the training analysis result and the preset analysis result is greater than or equal to a preset threshold value, successfully training the initial logistic regression model to obtain a pre-trained logistic regression model; when the similarity between the training analysis result and the preset analysis result is smaller than the preset threshold value, the initial logistic regression model is continuously trained by adjusting parameters in the initial logistic regression model until the initial logistic regression model is successfully trained.
As shown in fig. 3, fig. 3 is a schematic diagram of performing data cleaning and One-Hot independent encoding processing on list data provided in the embodiment of the present application, and performing word segmentation processing on list names in first preset list data or second preset list data, so as to obtain a word segmentation result, and performing One-Hot independent encoding processing on the word segmentation result. Classifying the list item features in the first preset list data or the second preset list data into a feature major class and a feature minor class with 2 dimensions, and performing One-Hot independent encoding treatment on the feature major class and the feature minor class respectively to obtain two-dimensional vectors (feature major class and feature minor class). Listing list units in the first preset list data or the second preset list data respectively, and performing One-Hot independent coding processing.
As shown in fig. 4, fig. 4 is an application schematic diagram of a logistic regression model provided in the embodiment of the present application, where the known list in fig. 4 is first list data, the known list in fig. 4 is second list data, and the list item features, list units and list names in the first list data and the second list data are respectively cleaned to obtain clean data, and then One-Hot independent encoding is performed on the clean data to obtain corresponding vectors. Specifically, a first vector of length 2 of the manifest item feature is obtained, a second vector of length 20 of the manifest name is obtained, and a third vector of length 1 of the manifest unit is obtained. The several vectors are combined into a combined vector (20,2,1), the content corresponding to the combined vector is (list name, list item feature, list unit), and the combined vector is input into a trained logistic regression model, so that the trained logistic regression model outputs an analysis result of whether a reference relationship exists between the first list data and the second list data.
As shown in fig. 5, fig. 5 is a schematic structural diagram of an apparatus for automatically matching a unit price reference to a price list according to an embodiment of the present application, and the embodiment of the present application further provides an apparatus for automatically matching a unit price reference to a price list, which includes:
a data acquisition unit 51 configured to acquire first list data and second list data in the engineering supervision database;
a data processing unit 52 configured to perform data preprocessing on the first list data to obtain first preprocessed data, and perform data preprocessing on the second list data to obtain second preprocessed data;
a data input unit 53 configured to input the first pre-processed data and the second pre-processed data to a pre-trained logistic regression model;
and an analysis result acquisition unit 54 configured to acquire an analysis result of whether or not there is a reference relationship between the first pre-processed data and the second pre-processed data output by the pre-trained logistic regression model.
It will be appreciated by those skilled in the art that 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.
Accordingly, the present application also proposes a computer readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in any of the embodiments of the present application.
Further, the application also proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, said processor implementing a method according to any of the embodiments of the application when executing said computer program.
The embodiment of the application provides electronic equipment. Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the present embodiment provides an electronic device 600, which includes: one or more processors 620; a storage device 610 for storing one or more programs that, when executed by the one or more processors 620, cause the one or more processors 620 to implement a method for automatically matching a cost manifest to a monovalent reference, the method comprising:
s101, acquiring first list data and second list data in an engineering supervision database.
S102, performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data.
S103, inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model.
S104, obtaining an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
The electronic device 600 shown in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 6, the electronic device 600 includes a processor 620, a storage device 610, an input device 630, and an output device 640; the number of processors 620 in the electronic device may be one or more, one processor 620 being taken as an example in fig. 6; the processor 620, the storage 610, the input 630, and the output 640 in the electronic device may be connected by a bus or other means, as exemplified in fig. 6 by a bus 650.
The storage device 610 is a computer readable storage medium, and may be used to store a software program, a computer executable program, and program instructions corresponding to a method for determining a cloud bottom height in the embodiments of the present application.
The storage device 610 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, the storage 610 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the storage device 610 may further include memory remotely located with respect to the processor 620, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may be used to receive input numeric, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 640 may include an electronic device such as a display screen, a speaker, etc.
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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method for automatically matching a price list to a monovalent reference, comprising:
acquiring first list data and second list data in an engineering supervision database;
performing data preprocessing on the first list data to obtain first preprocessed data, and performing data preprocessing on the second list data to obtain second preprocessed data;
inputting the first preprocessing data and the second preprocessing data into a pre-trained logistic regression model;
and acquiring an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
2. The method for automatically matching a monovalent reference to a bill of manufacturing cost according to claim 1, wherein the training mode of the logistic regression model comprises:
acquiring first preset list data and second preset list data in an engineering supervision database;
respectively carrying out data cleaning and data preprocessing on the first preset list data and the second preset list data to obtain a first preset vector and a second preset vector after cleaning and preprocessing;
inputting the first preset vector as a first training sample into an initial logistic regression model, and inputting the second preset vector as a second training sample into the initial logistic regression model;
acquiring a training analysis result of whether a reference relationship exists between the first preset list data and the second preset list data output by the initial logistic regression model;
when the similarity between the training analysis result and the preset analysis result is greater than or equal to a preset threshold value, the initial logistic regression model is successfully trained to obtain the pre-trained logistic regression model;
and when the similarity between the training analysis result and the preset analysis result is smaller than a preset threshold value, continuing training the initial logistic regression model by adjusting parameters in the initial logistic regression model until the initial logistic regression model is successfully trained.
3. The method for automatically matching a unit price reference to a manufacturing cost list according to claim 2, wherein the data cleaning and data preprocessing are performed on the first preset list data and the second preset list data respectively to obtain a first preset vector and a second preset vector after cleaning and preprocessing, and the method comprises the steps of:
performing data cleaning on the first preset list data to obtain first preset clean data, and performing One-Hot independent encoding on the first preset clean data to obtain a first preset vector;
and performing data cleaning on the second preset list data to obtain second preset clean data, and performing One-Hot independent encoding on the second preset clean data to obtain a second preset vector.
4. A method of automatically matching a monovalent reference to a bill of manufacturing costs according to claim 3, said method comprising:
dividing a plurality of list item features in the first preset list data or the second preset list data into a plurality of feature major classes according to at least two dimensions, wherein each feature major class comprises a plurality of different feature minor classes;
performing One-Hot independent coding treatment on the feature major class and the feature minor class respectively;
combining the One-Hot single-heat coding processing results of the feature major class and the feature minor class to obtain a first vector; wherein the first vector is a two-dimensional vector (feature big class, feature small class).
5. The method for automatically matching a monovalent reference to a bill of manufacturing cost according to claim 4, wherein said method further comprises:
performing word segmentation on the list names in the first preset list data or the second preset list data respectively to obtain word segmentation results;
performing One-Hot independent encoding treatment on the word segmentation result to obtain a second vector; wherein the second vector has a vector length of 20.
6. The method for automatically matching a monovalent reference to a bill of manufacturing cost according to claim 5, wherein said method further comprises:
performing One-Hot independent encoding processing on list units in the first preset list data or the second preset list data to obtain a third vector; wherein the third vector is a one-dimensional vector;
the first, second and third vectors are combined to obtain a combined vector (20,2,1).
7. The method of automatically matching a monovalent reference to a bill of manufacturing costs of claim 6, wherein inputting the first preset vector as a first training sample into an initial logistic regression model and inputting the second preset vector as a second training sample into an initial logistic regression model comprises:
taking the combined vector (20,2,1) of the first preset list data or the second preset list data as a first preset vector and a second preset vector respectively;
the combined vector (20,2,1) of the first preset inventory data is input as a first training sample into an initial logistic regression model, and the combined vector (20,2,1) of the second preset inventory data is input as a second training sample into an initial logistic regression model.
8. An apparatus for automatically matching a price list to a unit price reference, comprising:
the data acquisition unit is configured to acquire first list data and second list data in the engineering supervision database;
the data processing unit is configured to perform data preprocessing on the first list data to obtain first preprocessed data, and perform data preprocessing on the second list data to obtain second preprocessed data;
a data input unit configured to input the first pre-processed data and the second pre-processed data to a pre-trained logistic regression model;
and the analysis result acquisition unit is configured to acquire an analysis result of whether a reference relationship exists between the first preprocessing data and the second preprocessing data output by the pre-trained logistic regression model.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of automatically matching a monovalent reference to a price list according to any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a method for automatically matching a monovalent reference to a cost list as claimed in any one of claims 1 to 7 when the computer program is executed by the processor.
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