CN116205694B - Method, device, equipment and medium for automatic recommending mix proportion by cost quota - Google Patents
Method, device, equipment and medium for automatic recommending mix proportion by cost quota Download PDFInfo
- Publication number
- CN116205694B CN116205694B CN202310487850.8A CN202310487850A CN116205694B CN 116205694 B CN116205694 B CN 116205694B CN 202310487850 A CN202310487850 A CN 202310487850A CN 116205694 B CN116205694 B CN 116205694B
- Authority
- CN
- China
- Prior art keywords
- data
- quota
- vector
- mix
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000007781 pre-processing Methods 0.000 claims abstract description 22
- 238000003062 neural network model Methods 0.000 claims abstract description 9
- 239000013598 vector Substances 0.000 claims description 98
- 238000012549 training Methods 0.000 claims description 40
- 238000003860 storage Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004140 cleaning Methods 0.000 claims description 10
- 238000004519 manufacturing process Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000002156 mixing Methods 0.000 claims description 5
- 238000013329 compounding Methods 0.000 claims 1
- 238000012216 screening Methods 0.000 abstract description 3
- 239000000203 mixture Substances 0.000 description 69
- 238000012545 processing Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 10
- 230000006870 function Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Evolutionary Computation (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a method, a device, equipment and a medium for automatically recommending a proportioning ratio by a cost quota, wherein the method comprises the following steps: acquiring engineering quantity list data corresponding to the predicted quota; carrying out data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data; inputting target data into a pre-trained deep neural network model DNN; obtaining at least one target mix ratio output by a pre-trained DNN model; and screening the target mix proportion to be recommended from at least one target mix proportion according to the order of the priority, and recommending the target mix proportion to be recommended to the client. According to the embodiment of the application, when the user sets the quota in the existing engineering quantity list, the correct proportioning is automatically recommended to the user, the labor cost and the time cost are saved, and the proportioning selection efficiency and the accuracy are greatly improved.
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 recommending a blending ratio according to a cost quota.
Background
In the related art, most of engineering cost is calculated by manual metering, and part of engineering cost is calculated by combining with a computer. The user usually calculates the proportion by using the engineering supervision database, when the existing engineering quantity list is sleeved with the quota, when the proportion exists under the quota, the user is often required to manually select the corresponding proportion, the manual proportion selection mode not only increases the labor cost and the time cost, but also causes manual selection errors, and the proportion selection efficiency and the accuracy are lower.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for automatically recommending a proportion of a manufacturing cost quota, which can automatically recommend the correct proportion to a user when the user sleeves the quota in the existing engineering quantity list, save labor cost and time cost and greatly improve proportion selection efficiency and accuracy.
The method for automatically recommending the proportion of the manufacturing cost quota provided by the embodiment of the application comprises the following steps:
acquiring engineering quantity list data corresponding to a predicted quota;
performing data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
inputting the target data into a pre-trained deep neural network model DNN;
acquiring at least one target mix ratio output by the pre-trained DNN model;
and screening target mix ratios to be recommended from the at least one target mix ratio according to the order of the priority, and recommending the target mix ratios to be recommended to the client.
In some embodiments, the training manner of the DNN model includes:
acquiring preset quota data in an engineering supervision database and preset engineering quantity list data matched with the preset quota data;
performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data;
inputting the cleaned and preprocessed preset quota data and engineering quantity list data as training samples into an initial DNN model, and obtaining a training mix ratio output by the initial DNN model;
when the difference value between the training mix proportion and the preset mix proportion is smaller than a preset threshold value, the initial DNN model is successfully trained to obtain the pre-trained DNN model;
and when the difference value between the training mix proportion and the preset mix proportion is larger than or equal to a preset threshold value, continuing training the initial DNN model by adjusting parameters in the initial DNN model until the initial DNN model is successfully trained.
In some embodiments, performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data, including:
dividing the list item characteristics in the preset engineering quantity list data into a plurality of characteristic major classes and characteristic minor classes according to at least two dimensions, wherein each characteristic major class comprises a plurality of different characteristic minor classes;
and performing One-Hot encoding on-Hot processing on the feature major class and the feature minor class respectively, and merging the One-Hot 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 major class and feature minor class).
In some embodiments, the method further comprises:
dividing words of quota names in the preset quota data respectively to obtain word dividing results;
and performing One-Hot processing on the word segmentation result to obtain a second vector, wherein the vector length of the second vector is 20.
In some embodiments, the method further comprises:
respectively performing One-Hot processing on a list unit and a quota unit in the preset engineering quantity list data to obtain a third vector corresponding to the list unit and a fourth vector corresponding to the quota unit, wherein the third vector and the fourth vector are One-dimensional vectors;
the second vector, the first vector, the fourth vector and the third vector are combined to obtain a combined vector (20,2,1,1).
In some embodiments, the method further comprises:
dividing the actual proportion corresponding to the preset quota data into a plurality of proportion major classes and proportion minor classes, wherein each proportion major class comprises a plurality of different proportion minor classes;
and carrying out One-Hot encoding on-Hot processing on the major class and the minor class of the proportion respectively, and merging the on-Hot processing results of the major class and the minor class of the proportion to obtain a fifth vector, wherein the fifth vector is a two-dimensional vector (major class of the proportion and minor class of the proportion).
In some embodiments, inputting the cleaned and preprocessed preset quota data and engineering quantity list data as training samples into an initial DNN model, and obtaining a training mix output by the initial DNN model, including:
inputting the combined vector (20,2,1,1) as training input data into the initial DNN model, and inputting the fifth vector (major class, minor class) as training output data into the initial DNN model, the fifth vector representing the preset blending ratio;
acquiring a training mix ratio output by the initial DNN model;
a difference between the training mix and the fifth vector as training output data is calculated.
The embodiment of the application also provides a device for automatically recommending the proportion of the manufacturing cost quota, which comprises:
the data acquisition unit is configured to acquire a predicted quota and engineering quantity list data corresponding to the predicted quota;
the data preprocessing unit is configured to perform data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
a data input unit configured to input the target data to a pre-trained deep neural network model DNN;
a mix ratio obtaining unit configured to obtain at least one target mix ratio output by the pre-trained DNN model;
and the mix proportion recommending unit is configured to screen out target mix proportions to be recommended from the at least one target mix proportion according to the order of the priority level and recommend the target mix proportions to be recommended to the client.
The embodiment of the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of automatic recommendation of a mix of manufacturing cost quota as described above.
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 on the processor, wherein the processor realizes the method for automatically recommending the proportioning ratio according to the cost quota when executing the computer program.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
carrying out data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data; inputting target data into a pre-trained deep neural network model DNN; obtaining at least one target mix ratio output by a pre-trained DNN model; and selecting the target mix proportion to be recommended from at least one target mix proportion according to the order of the priority, and recommending the target mix proportion to be recommended to the client, so that when the user is in the rated state in the existing engineering quantity list, the correct mix proportion is automatically recommended to the user, the labor cost and the time cost are saved, and the mix proportion selection efficiency and the accuracy are greatly improved.
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 specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a method for automatically recommending a mix ratio for a price quota according to an embodiment of the application;
FIG. 2 is a schematic diagram of data cleansing and data preprocessing according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a training process of a DNN model according to an embodiment of the present application;
fig. 4 is a schematic diagram of an application process of a DNN model according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an apparatus for automatic recommended blending ratio based on a cost ration 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
In order to make the objects, technical solutions and advantages of the present application more apparent, 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, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
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 recommending a proportion of a price ratio according to an embodiment of the present application, where the method for automatically recommending a proportion of a price ratio according to an embodiment of the present application includes:
s101, acquiring engineering quantity list data corresponding to a predicted quota;
s102, carrying out data preprocessing on predicted quota and engineering quantity list data to obtain preprocessed target data;
s103, inputting target data into a pre-trained deep neural network model DNN;
s104, obtaining at least one target mix ratio output by a pre-trained DNN model;
s105, selecting a target mix to be recommended from at least one target mix according to the order of the priority, and recommending the target mix to be recommended to the client.
Optionally, as the selection of the mix proportion during the set quota is related to the content of the engineering quantity list data, intelligent word segmentation is performed on the content of the engineering quantity list data and keywords in the matching configuration library are automatically extracted through the well-entered or imported engineering quantity list data, first round of analysis recommendation is performed by using user habits, when multiple options are analyzed, the option with the highest use probability is analyzed by combining the distribution probability of the large database, and the target mix proportion used with the highest probability is recommended.
According to the embodiment of the application, when the user sets the quota in the existing engineering quantity list, the correct proportioning is automatically recommended to the user, the manual selection of the proportioning by the user is avoided, the labor cost and the time cost are saved, and the proportioning selection efficiency and the accuracy are greatly improved.
In some embodiments, the training manner of the DNN model of the embodiments of the present application includes:
acquiring preset quota data and preset engineering quantity list data matched with the preset quota data in an engineering supervision database;
performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data;
inputting the cleaned and preprocessed preset quota data and engineering quantity list data as training samples into an initial DNN model, and obtaining a training mix ratio output by the initial DNN model;
when the difference value between the training mix ratio and the preset mix ratio is smaller than a preset threshold value, the initial DNN model is successfully trained, and a pre-trained DNN model is obtained;
when the difference value between the training mix ratio and the preset mix ratio is larger than or equal to a preset threshold value, the initial DNN model is continuously trained by adjusting parameters in the initial DNN model until the initial DNN model is successfully trained.
It should be noted that, the preset threshold may be set according to an actual service requirement, which is not limited in the embodiment of the present application.
Optionally, the DNN model of the embodiment of the present application is provided with 32 hidden layers.
Alternatively, the engineering proctorial database may contain information of the manifest unit price reference and information corresponding to the manifest.
In some embodiments, performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data, including:
dividing the list item characteristics in the preset engineering quantity list data into a plurality of characteristic major classes and characteristic minor classes according to at least two dimensions, wherein each characteristic major class comprises a plurality of different characteristic minor classes;
and performing One-Hot encoding on-Hot processing on the feature major class and the feature minor class respectively, and merging the on-Hot 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 major class and feature minor class).
In some embodiments, the method further comprises:
dividing words of quota names in preset quota data respectively to obtain word dividing results;
and performing One-Hot processing on the word segmentation result to obtain a second vector, wherein the vector length of the second vector is 20.
In some embodiments, the method further comprises:
respectively performing One-Hot processing on a list unit and a quota unit in preset engineering quantity list data to obtain a third vector corresponding to the list unit and a fourth vector corresponding to the quota unit, wherein the third vector and the fourth vector are One-dimensional vectors;
the second vector, the first vector, the fourth vector and the third vector are combined to obtain a combined vector (20,2,1,1).
In some embodiments, the method further comprises:
dividing the actual proportion corresponding to the preset quota data into a plurality of proportion major classes and proportion minor classes, wherein each proportion major class comprises a plurality of different proportion minor classes;
and (3) performing One-Hot encoding One-Hot processing on the major class and the minor class of the coordination ratio respectively, and merging the One-Hot processing results of the major class and the minor class of the coordination ratio to obtain a fifth vector, wherein the fifth vector is a two-dimensional vector (major class of the coordination ratio and minor class of the coordination ratio).
Alternatively, the major and minor classes of all mix ratios in the engineering supervision database are listed, for example, pure concrete and pump pure concrete are two major classes, "pump pure concrete C15.5" and "pump pure concrete C20.5" are two minor classes, and data cleaning and One-Hot encoding are performed on all mix ratios in the library to generate One two-dimensional vector (mix ratio major class, mix ratio minor class).
In some embodiments, inputting the cleaned and preprocessed preset quota data and engineering quantity inventory data as training samples into an initial DNN model, and obtaining a training mix output by the initial DNN model, including:
inputting the combined vector (20,2,1,1) as training input data into the initial DNN model, and inputting a fifth vector (major class of mix, minor class of mix) as training output data into the initial DNN model, the fifth vector representing a preset mix;
acquiring a training mix ratio output by an initial DNN model;
a difference between the training mix and a fifth vector, which is training output data, is calculated.
As shown in fig. 2, a schematic diagram of data cleaning and data preprocessing according to an embodiment of the present application is shown, where the data cleaning process and the data preprocessing process include:
and performing word segmentation on all quota names in the engineering supervision database to obtain word segmentation results, and performing One-Hot coding on the word segmentation results to generate a second vector with the vector length of 20.
Classifying all list item features in the engineering supervision database according to two dimensions, dividing the list item features into a feature major class and a feature minor class, and performing One-Hot coding to generate a first vector (the feature major class and the feature minor class) with a vector length of 2.
Enumerating all list units in the engineering supervision database, and performing One-Hot coding to obtain a third vector.
Enumerating all quota units in the engineering supervision database, and performing One-Hot coding to obtain a fourth vector.
And (3) dividing the size class of all the actual mix ratios in the engineering supervision database, and performing One-Hot coding to obtain a fifth vector (mix ratio major class, mix ratio minor class) with the vector length of 2.
As shown in fig. 3, a schematic diagram of a training process of a DNN model provided in an embodiment of the present application is shown, based on a second vector, a first vector, a fourth vector and a third vector obtained by data cleaning and data preprocessing in fig. 2, the second vector, the first vector, the fourth vector and the third vector are combined to obtain a combined vector (20,2,1,1), the contents corresponding to the combined vector are (a rating name, a list item classification, a rating unit and a list unit) respectively, the combined vector is input into an initial DNN model, all actual mix ratios in an engineering supervision database are classified into large and small classes, and a fifth vector (a mix ratio large class and a mix ratio small class) obtained by One-Hot encoding is input into the initial DNN model, and model training is performed to obtain a trained DNN model.
As shown in fig. 4, an application process schematic diagram of the DNN model provided by the embodiment of the present application is shown, where on-Hot encoding is performed on predicted quota and engineering amount inventory data, specifically on-Hot encoding is performed on quota name, inventory item feature, quota unit, and inventory unit data, vectors with vector lengths of 20,2,1, and 1 are respectively obtained, the vectors are combined to obtain a combined vector (20,2,1,1), the contents corresponding to the combined vector are (quota name, inventory item category, quota unit, inventory unit), and the combined vector is input to the trained DNN model, so as to obtain at least One target mix output by the trained DNN model, the target mix to be recommended is selected from the at least One target mix according to the order of priority, and the target mix to be recommended is recommended to the client.
As shown in fig. 5, the embodiment of the present application further provides an apparatus for automatically recommending a blending ratio according to a cost quota, including:
a data acquisition unit 51 configured to acquire a predicted quota and engineering quantity list data corresponding to the predicted quota;
a data preprocessing unit 52 configured to perform data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
a data input unit 53 configured to input target data to a pre-trained deep neural network model DNN;
a mix acquisition unit 54 configured to acquire at least one target mix output by the pre-trained DNN model;
and a mix recommendation unit 55 configured to screen out target mix to be recommended from at least one target mix in order of priority, and recommend the target mix to be recommended to the client.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application therefore 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 according to any of the embodiments of the application.
Furthermore, the application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of being run by the processor, wherein the processor executes the computer program to realize the method according to any embodiment of the application.
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 recommending a mix for a price quota provided by an embodiment of the application, the method comprising:
acquiring engineering quantity list data corresponding to a predicted quota;
performing data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
inputting the target data into a pre-trained deep neural network model DNN;
acquiring at least one target mix ratio output by the pre-trained DNN model;
and screening target mix ratios to be recommended from the at least one target mix ratio according to the order of the priority, and recommending the target mix ratios to be recommended to the client.
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 a module unit, such as program instructions corresponding to a method for determining a cloud bottom height in an embodiment 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. 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 variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (6)
1. A method for automatically recommending a mix for a price quota, comprising:
acquiring engineering quantity list data corresponding to a predicted quota;
performing data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
inputting the target data into a pre-trained deep neural network model DNN;
acquiring at least one target mix ratio output by the pre-trained DNN model;
selecting a target mix to be recommended from the at least one target mix according to the order of the priority, and recommending the target mix to be recommended to a client;
the training mode of the DNN model comprises the following steps:
acquiring preset quota data in an engineering supervision database and preset engineering quantity list data matched with the preset quota data;
performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data;
inputting the cleaned and preprocessed preset quota data and engineering quantity list data as training samples into an initial DNN model, and obtaining a training mix ratio output by the initial DNN model;
when the difference value between the training mix proportion and the preset mix proportion is smaller than a preset threshold value, the initial DNN model is successfully trained to obtain the pre-trained DNN model;
when the difference value between the training mix proportion and the preset mix proportion is larger than or equal to a preset threshold value, continuing to train the initial DNN model by adjusting parameters in the initial DNN model until the initial DNN model is successfully trained;
performing data cleaning and data preprocessing on the preset quota data and the preset engineering quantity list data to obtain cleaned and preprocessed engineering quantity list data, wherein the method comprises the following steps of:
dividing the list item characteristics in the preset engineering quantity list data into a plurality of characteristic major classes and characteristic minor classes according to at least two dimensions, wherein each characteristic major class comprises a plurality of different characteristic minor classes;
performing independent heat coding treatment on the feature major class and the feature minor class respectively, and merging independent heat coding treatment 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 major class and feature minor class);
performing independent heat coding treatment on a list unit and a quota unit in the preset engineering quantity list data respectively to obtain a third vector corresponding to the list unit and a fourth vector corresponding to the quota unit, wherein the third vector and the fourth vector are one-dimensional vectors;
combining a second vector, a first vector, a fourth vector and a third vector to obtain a combined vector (20,2,1,1), wherein the second vector is a vector corresponding to a quota name in the preset quota data;
inputting the cleaned and preprocessed preset quota data and engineering quantity list data as training samples into an initial DNN model, and obtaining training mix ratios output by the initial DNN model, wherein the method comprises the following steps:
inputting the combined vector (20,2,1,1) as training input data into the initial DNN model, and inputting a fifth vector (major class, minor class) as training output data into the initial DNN model, the fifth vector representing the preset blending ratio;
acquiring a training mix ratio output by the initial DNN model;
a difference between the training mix and the fifth vector as training output data is calculated.
2. The method for automatically recommending a mix for a manufacturing cost quota of claim 1, further comprising:
dividing words of quota names in the preset quota data respectively to obtain word dividing results;
and performing single-heat encoding treatment on the word segmentation result to obtain a second vector, wherein the vector length of the second vector is 20.
3. The method for automatically recommending a mix for a manufacturing cost quota of claim 1, further comprising:
dividing the actual proportion corresponding to the preset quota data into a plurality of proportion major classes and proportion minor classes, wherein each proportion major class comprises a plurality of different proportion minor classes;
and respectively carrying out single-heat coding treatment on the major class and the minor class of the proportion, and merging the single-heat coding treatment results of the major class and the minor class of the proportion to obtain a fifth vector, wherein the fifth vector is a two-dimensional vector (major class of the proportion and minor class of the proportion).
4. A device for automatically recommending a mix for a price ratio according to any one of claims 1 to 3, characterized in that it comprises:
the data acquisition unit is configured to acquire a predicted quota and engineering quantity list data corresponding to the predicted quota;
the data preprocessing unit is configured to perform data preprocessing on the predicted quota and the engineering quantity list data to obtain preprocessed target data;
a data input unit configured to input the target data to a pre-trained deep neural network model DNN;
a mix ratio obtaining unit configured to obtain at least one target mix ratio output by the pre-trained DNN model;
and the mix proportion recommending unit is configured to screen out target mix proportions to be recommended from the at least one target mix proportion according to the order of the priority level and recommend the target mix proportions to be recommended to the client.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method of automatic recommended compounding of a cost quota according to any one of claims 1 to 3.
6. 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 of automatically recommending a mix of manufacturing cost ratings according to any one of claims 1 to 3 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310487850.8A CN116205694B (en) | 2023-05-04 | 2023-05-04 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310487850.8A CN116205694B (en) | 2023-05-04 | 2023-05-04 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116205694A CN116205694A (en) | 2023-06-02 |
| CN116205694B true CN116205694B (en) | 2023-10-24 |
Family
ID=86508047
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310487850.8A Active CN116205694B (en) | 2023-05-04 | 2023-05-04 | Method, device, equipment and medium for automatic recommending mix proportion by cost quota |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116205694B (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934527A (en) * | 2017-02-22 | 2017-07-07 | 广东华联软件科技有限公司 | A kind of enterprise model system system and method |
| CN107578332A (en) * | 2017-09-22 | 2018-01-12 | 深圳乐信软件技术有限公司 | A kind of method, apparatus, equipment and storage medium for recommending cash commodity |
| CN109493137A (en) * | 2018-11-06 | 2019-03-19 | 东北大学 | Conclusion of the business based on deep learning is fixed a price recommended method in a kind of industry supply chain |
| CN110147820A (en) * | 2019-04-11 | 2019-08-20 | 北京远航通信息技术有限公司 | Recommended method, device, equipment and the storage medium of the additional oil mass of flight |
| CN110163430A (en) * | 2019-05-10 | 2019-08-23 | 东南大学 | Concrete material Prediction of compressive strength method based on AdaBoost algorithm |
| CN110851462A (en) * | 2019-11-06 | 2020-02-28 | 福建省晨曦信息科技股份有限公司 | One-key pricing method and terminal based on engineering data |
| CN112949907A (en) * | 2021-02-04 | 2021-06-11 | 杭州品茗安控信息技术股份有限公司 | Quota matching method, device, equipment and storage medium for engineering cost |
| CN112949906A (en) * | 2021-02-04 | 2021-06-11 | 杭州品茗安控信息技术股份有限公司 | Matching method, device, equipment and storage medium for engineering cost quota conversion |
| CN113761375A (en) * | 2021-09-10 | 2021-12-07 | 未鲲(上海)科技服务有限公司 | Message recommendation method, device, equipment and storage medium based on neural network |
| CN113763089A (en) * | 2020-10-27 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Article recommendation method and device and computer-readable storage medium |
| CN113806392A (en) * | 2021-08-24 | 2021-12-17 | 广联达科技股份有限公司 | Building project list data processing method, project pricing method and device |
| CN113886691A (en) * | 2021-09-29 | 2022-01-04 | 平安银行股份有限公司 | Intelligent recommendation method and device based on historical data, electronic equipment and medium |
| CN114049172A (en) * | 2021-11-08 | 2022-02-15 | 土巴兔集团股份有限公司 | Item recommendation sorting method and device, computer equipment and storage medium |
| CN114429245A (en) * | 2022-01-17 | 2022-05-03 | 国网湖北省电力有限公司经济技术研究院 | Analysis display method of engineering cost data |
| CN115358465A (en) * | 2022-08-19 | 2022-11-18 | 镇江市金舟软件有限责任公司 | Ship welding quota working hour rapid prediction method and system, electronic equipment and storage medium |
| CN115687331A (en) * | 2022-11-16 | 2023-02-03 | 江苏鸿程大数据技术与应用研究院有限公司 | Intelligent matching method and system for engineering cost quota |
| CN115841267A (en) * | 2022-09-29 | 2023-03-24 | 陕西交建云数据科技有限公司 | Enterprise quota correction method of multi-layer BP neural network |
| CN115936801A (en) * | 2022-11-10 | 2023-04-07 | 中国平安财产保险股份有限公司 | Neural network-based product recommendation method, device, equipment and storage medium |
| CN115964954A (en) * | 2023-01-10 | 2023-04-14 | 上海理工大学 | Ultrahigh-performance concrete multi-item performance prediction method and mix proportion optimization design method thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190228397A1 (en) * | 2018-01-25 | 2019-07-25 | The Bartley J. Madden Foundation | Dynamic economizer methods and systems for improving profitability, savings, and liquidity via model training |
-
2023
- 2023-05-04 CN CN202310487850.8A patent/CN116205694B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934527A (en) * | 2017-02-22 | 2017-07-07 | 广东华联软件科技有限公司 | A kind of enterprise model system system and method |
| CN107578332A (en) * | 2017-09-22 | 2018-01-12 | 深圳乐信软件技术有限公司 | A kind of method, apparatus, equipment and storage medium for recommending cash commodity |
| CN109493137A (en) * | 2018-11-06 | 2019-03-19 | 东北大学 | Conclusion of the business based on deep learning is fixed a price recommended method in a kind of industry supply chain |
| CN110147820A (en) * | 2019-04-11 | 2019-08-20 | 北京远航通信息技术有限公司 | Recommended method, device, equipment and the storage medium of the additional oil mass of flight |
| CN110163430A (en) * | 2019-05-10 | 2019-08-23 | 东南大学 | Concrete material Prediction of compressive strength method based on AdaBoost algorithm |
| CN110851462A (en) * | 2019-11-06 | 2020-02-28 | 福建省晨曦信息科技股份有限公司 | One-key pricing method and terminal based on engineering data |
| CN113763089A (en) * | 2020-10-27 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Article recommendation method and device and computer-readable storage medium |
| CN112949907A (en) * | 2021-02-04 | 2021-06-11 | 杭州品茗安控信息技术股份有限公司 | Quota matching method, device, equipment and storage medium for engineering cost |
| CN112949906A (en) * | 2021-02-04 | 2021-06-11 | 杭州品茗安控信息技术股份有限公司 | Matching method, device, equipment and storage medium for engineering cost quota conversion |
| CN113806392A (en) * | 2021-08-24 | 2021-12-17 | 广联达科技股份有限公司 | Building project list data processing method, project pricing method and device |
| CN113761375A (en) * | 2021-09-10 | 2021-12-07 | 未鲲(上海)科技服务有限公司 | Message recommendation method, device, equipment and storage medium based on neural network |
| CN113886691A (en) * | 2021-09-29 | 2022-01-04 | 平安银行股份有限公司 | Intelligent recommendation method and device based on historical data, electronic equipment and medium |
| CN114049172A (en) * | 2021-11-08 | 2022-02-15 | 土巴兔集团股份有限公司 | Item recommendation sorting method and device, computer equipment and storage medium |
| CN114429245A (en) * | 2022-01-17 | 2022-05-03 | 国网湖北省电力有限公司经济技术研究院 | Analysis display method of engineering cost data |
| CN115358465A (en) * | 2022-08-19 | 2022-11-18 | 镇江市金舟软件有限责任公司 | Ship welding quota working hour rapid prediction method and system, electronic equipment and storage medium |
| CN115841267A (en) * | 2022-09-29 | 2023-03-24 | 陕西交建云数据科技有限公司 | Enterprise quota correction method of multi-layer BP neural network |
| CN115936801A (en) * | 2022-11-10 | 2023-04-07 | 中国平安财产保险股份有限公司 | Neural network-based product recommendation method, device, equipment and storage medium |
| CN115687331A (en) * | 2022-11-16 | 2023-02-03 | 江苏鸿程大数据技术与应用研究院有限公司 | Intelligent matching method and system for engineering cost quota |
| CN115964954A (en) * | 2023-01-10 | 2023-04-14 | 上海理工大学 | Ultrahigh-performance concrete multi-item performance prediction method and mix proportion optimization design method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116205694A (en) | 2023-06-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110321422A (en) | Method, method for pushing, device and the equipment of on-line training model | |
| CN111611488B (en) | Information recommendation method and device based on artificial intelligence and electronic equipment | |
| CN111460290B (en) | Information recommendation method, device, equipment and storage medium | |
| CN109800328B (en) | Video recommendation method, device thereof, information processing equipment and storage medium | |
| CN110019298A (en) | Data processing method and device | |
| CN109348262A (en) | A kind of calculation method, device, equipment and the storage medium of main broadcaster's similarity | |
| CN108733694B (en) | Retrieval recommendation method and device | |
| CN108446296B (en) | Information processing method and device | |
| CN116205694B (en) | Method, device, equipment and medium for automatic recommending mix proportion by cost quota | |
| CN113327132A (en) | Multimedia recommendation method, device, equipment and storage medium | |
| CN108228869A (en) | The method for building up and device of a kind of textual classification model | |
| CN111597383A (en) | Video heat level prediction method and device | |
| CN111966916A (en) | Recommendation method and device, electronic equipment and computer readable storage medium | |
| CN114329093B (en) | Data processing method, device and equipment | |
| CN114385876B (en) | Model search space generation method, device and system | |
| CN111831892A (en) | Information recommendation method, information recommendation device, server and storage medium | |
| CN108460475A (en) | Poor student's prediction technique and device based on network playing by students behavior | |
| CN114968028A (en) | Method, apparatus, and medium for dynamically changing menu layout based on user behavior analysis | |
| CN111291904B (en) | Preference prediction method and device and computer equipment | |
| CN110717109B (en) | Method, device, electronic equipment and storage medium for recommending data | |
| CN110674330B (en) | Expression management method and device, electronic equipment and storage medium | |
| CN113641897A (en) | Recommendation method and device based on session text, electronic equipment and storage medium | |
| CN116188091A (en) | Method, device, equipment and medium for automatic matching unit price reference of cost list | |
| CN114611015B (en) | Interactive information processing method and device and cloud server | |
| CN117217852B (en) | Behavior recognition-based purchase willingness prediction method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |