CN112434882A - Expert extraction demand prediction method and device based on time series prediction - Google Patents

Expert extraction demand prediction method and device based on time series prediction Download PDF

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CN112434882A
CN112434882A CN202011453019.3A CN202011453019A CN112434882A CN 112434882 A CN112434882 A CN 112434882A CN 202011453019 A CN202011453019 A CN 202011453019A CN 112434882 A CN112434882 A CN 112434882A
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time sequence
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佟忠正
赵永发
林俊
王泽涌
洪雨天
黄杰韬
王喆
吴赟
臧笑宇
陈非
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Guangdong Electric Power Information Technology Co Ltd
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Abstract

The invention discloses an expert extraction demand prediction method based on time series prediction, which specifically comprises the following steps: collecting historical data time series of historical time periods of a bidding system from a database, and counting the time series requirements of regions, professions and titles extracted by the experts on the basis of the historical data time series; carrying out wiener filtering on the historical data time sequence and carrying out normalization processing to obtain preprocessed data; acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network; and performing reverse normalization on the time series demand prediction values of the normalized regions, the specialties and the titles to obtain the demand prediction of expert extraction under the bidding system. The method can realize accurate prediction of the requirement of the quantity of the expert extracted in the future time period, prediction of the peak value of the expert extracted, and warning of peak-off review of project service personnel, thereby realizing intelligent expert extraction planning.

Description

Expert extraction demand prediction method and device based on time series prediction
Technical Field
The invention relates to the technical field of demand prediction, in particular to an expert extraction demand prediction method and device based on time series prediction.
Background
And (4) comparing and analyzing the bid documents of all the suppliers by bid evaluation experts in the bid inviting system, and selecting the best supplier from the bid documents. Before bid evaluation, an area for extracting experts is selected, and then the experts are extracted from an expert database of the area. At present, when a plurality of large-scale projects of the same type are subjected to centralized bidding, expert extraction is difficult or high-quality expert extraction is difficult, so that expert extraction demand prediction is particularly important.
Disclosure of Invention
In order to solve the problems, the invention provides an expert extraction demand prediction method and device based on time series prediction, which can realize accurate prediction.
In order to solve the above technical problem, a first aspect of the present invention provides a method for predicting expert extraction demand based on time series prediction, including the following steps:
s1, collecting a historical data time sequence of the bidding system historical time period from the database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts;
s2, counting the regional time sequence requirements, professional time sequence requirements and job title time sequence requirements extracted by the experts on the basis of the historical data time sequence;
s3, carrying out wiener filtering and normalization processing on the historical data time sequence to obtain preprocessed data;
s4, acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network;
and S5, performing reverse normalization on the normalized regional time sequence demand predicted value, the professional time sequence demand predicted value and the job title time sequence demand predicted value to obtain expert extraction demand prediction under a bidding system.
Preferably, step S2 is specifically: the bidding system is divided into rectangular grids with set sizes, the historical data time sequence is divided into time blocks with set lengths, the experts are extracted and divided into regional time sequence requirements, professional time sequence requirements and job time sequence requirements, classification and summary statistics of the historical data time sequence are carried out on each time block of each rectangular grid area, and the regional time sequence requirements, the professional time sequence requirements and the job time sequence requirements extracted by the experts corresponding to each time block of each grid area are obtained and used as sample sets.
Preferably, in step S3, the wiener filtering process performs denoising on the historical data time series; the wiener filtering calculation formula is
Figure 186917DEST_PATH_IMAGE001
Wherein,
Figure 415641DEST_PATH_IMAGE002
as the initial data, it is,
Figure 460958DEST_PATH_IMAGE003
adding wiener filtering to the initial data to denoise the noise,
Figure 387326DEST_PATH_IMAGE004
（0，1]in order to be a noise factor, the noise factor,
Figure 483589DEST_PATH_IMAGE005
is a random number that is normally too distributed,
Figure 571630DEST_PATH_IMAGE006
（0，1）。
preferably, in step S3, the normalized calculation formula is:
Figure 104243DEST_PATH_IMAGE007
wherein R isiIn order to normalize the new value of the variable,
Figure 568722DEST_PATH_IMAGE008
and R is a variable value.
Preferably, step S4 is specifically: dividing the preprocessed sample set into a training set and a testing set; and for each network in the expert extraction condition prediction deep neural network, training and adjusting the structure and parameters of the network through a training set, and performing forward calculation on the test set through each network in the condition prediction deep neural network extracted by each category of experts so as to obtain the average prediction error of the historical data time sequence on the test set through the deep neural network.
Preferably, the calculation formula of the average prediction error is:
Figure 768759DEST_PATH_IMAGE009
wherein N represents the number of predicted values obtained by an expert in the historical time period on the test set by extracting the predicted deep neural network,
Figure 27702DEST_PATH_IMAGE010
and
Figure 47611DEST_PATH_IMAGE011
and extracting a predicted value and a corresponding real time sequence value which are obtained on the test set through the deep neural network for the current expert.
Preferably, the denormalization calculation formula is:
Figure 299470DEST_PATH_IMAGE012
wherein R is the inverse normalized variable prediction time series value, and R is the normalized variable prediction time series value.
The invention provides a prediction device for expert extraction demand based on time series prediction, which comprises:
a data acquisition module: the data acquisition module is used for acquiring a historical data time sequence of a historical time period of the bidding system from a database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts;
a statistical data module: the statistical data module is used for counting the region time sequence requirement, the professional time sequence requirement and the job title time sequence requirement extracted by the expert based on the historical data time sequence;
a data preprocessing module: the data preprocessing module is used for carrying out wiener filtering and normalization processing on the historical data time sequence to obtain preprocessed data;
a neural network module: acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network;
a demand forecasting module: the demand forecasting module is used for carrying out reverse normalization on the normalized region time sequence demand forecasting value, the professional time sequence demand forecasting value and the job time sequence demand forecasting value to obtain expert extraction demand forecasting under the bidding system.
A third aspect of the present invention provides a terminal, including a processor and a memory, where the memory stores a computer program, and the processor is configured to execute the computer program to implement any one of the expert extraction demand prediction methods based on time series prediction.
A fourth aspect of the present invention provides a storage medium storing a computer program executable to implement any one of the time-series prediction-based expert extraction demand prediction methods described above.
Compared with the prior art, the invention has the beneficial effects that:
by combining normalization and a deep neural network, the requirements of accurately predicting the quantity of the experts extracted in the future time period, predicting the peak value of the expert extraction and warning the peak error evaluation of project service personnel can be realized, so that the intelligent expert extraction planning is realized.
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Fig. 1 is a flowchart of an expert extraction demand prediction method based on time series prediction according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an expert extraction demand prediction apparatus based on time series prediction according to an embodiment of the present invention.
Detailed Description
The following examples are further illustrative of the present invention and are not intended to be limiting thereof.
Referring to fig. 1, a first aspect of the present invention provides a method for predicting expert extraction demand based on time series prediction, including the following steps:
s1, collecting a historical data time sequence of the bidding system historical time period from the database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts.
S2, counting the regional time sequence requirements, professional time sequence requirements and job title time sequence requirements extracted by the experts on the basis of the historical data time sequence;
further, in the embodiment of the present invention, step S2 specifically includes: the bidding system is divided into rectangular grids with set sizes, the historical data time sequence is divided into time blocks with set lengths, the experts are extracted and divided into regional time sequence requirements, professional time sequence requirements and job time sequence requirements, classification and summary statistics of the historical data time sequence are carried out on each time block of each rectangular grid area, and the regional time sequence requirements, the professional time sequence requirements and the job time sequence requirements extracted by the experts corresponding to each time block of each grid area are obtained and used as sample sets.
And S3, carrying out wiener filtering on the historical data time sequence and carrying out normalization processing to obtain preprocessed data.
Further, in step S3, the wiener filtering process performs denoising on the historical data time series.
Further, in step S3 of the embodiment of the present invention, the calculation formula of the wiener filter for denoising the noise is as follows:
Figure 88434DEST_PATH_IMAGE013
wherein,
Figure 315016DEST_PATH_IMAGE014
as the initial data, it is,
Figure 87800DEST_PATH_IMAGE015
is composed of
Figure 910394DEST_PATH_IMAGE016
Initial data is added with data subjected to noise removal by wiener filtering,
Figure 819444DEST_PATH_IMAGE017
（0，1]in order to be a noise factor, the noise factor,
Figure 685769DEST_PATH_IMAGE018
is a random number that is normally too distributed,
Figure 680270DEST_PATH_IMAGE019
（0，1）。
further, in step S3 of the embodiment of the present invention, the normalized calculation formula is:
Figure 24663DEST_PATH_IMAGE020
wherein R isiIn order to normalize the new value of the variable,
Figure 788220DEST_PATH_IMAGE021
and R is a variable value.
And analyzing the requirement rules of the experts in different periods according to the data extracted by the experts in the historical data time sequence. For example: the demand of months in the year is vigorous, the demand in the future time period is predicted by constructing a prediction model, and finally, business personnel are reminded of peak-off bid evaluation by comparing with the existing experts in the database.
And S4, acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network.
Further, in the embodiment of the present invention, step S4 specifically includes: dividing the preprocessed sample set into a training set and a testing set; and for each network in the expert extraction condition prediction deep neural network, training and adjusting the structure and parameters of the network through a training set, and performing forward calculation on the test set through each network in the condition prediction deep neural network extracted by each category of experts so as to obtain the average prediction error of the historical data time sequence on the test set through the deep neural network.
Further, in the embodiment of the present invention, the calculation formula of the average prediction error is:
Figure 74714DEST_PATH_IMAGE022
wherein N represents the number of predicted values obtained by an expert in the historical time period on the test set by extracting the predicted deep neural network,
Figure 556511DEST_PATH_IMAGE023
and
Figure 704595DEST_PATH_IMAGE024
and extracting a predicted value and a corresponding real time sequence value which are obtained on the test set through the deep neural network for the current expert.
And S5, performing reverse normalization on the normalized regional time sequence demand predicted value, the professional time sequence demand predicted value and the job title time sequence demand predicted value to obtain expert extraction demand prediction under a bidding system.
Further, in the embodiment of the present invention, the inverse normalization calculation formula is:
Figure 322658DEST_PATH_IMAGE025
wherein R is the inverse normalized variable prediction time series value, and R is the normalized variable prediction time series value.
By combining normalization and a deep neural network, the requirements of accurately predicting the quantity of the experts extracted in the future time period, predicting the peak value of the expert extraction and warning the peak error evaluation of project service personnel can be realized, so that the intelligent expert extraction planning is realized.
Referring to fig. 2, an embodiment of the present invention provides an expert extraction demand prediction apparatus based on time series prediction, including:
the data acquisition module 201: the data acquisition module is used for acquiring a historical data time sequence of a historical time period of the bidding system from a database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts;
the statistical data module 202: the statistical data module is used for counting the region time sequence requirement, the professional time sequence requirement and the job title time sequence requirement extracted by the expert based on the historical data time sequence;
the data preprocessing module 203: the data preprocessing module is used for carrying out wiener filtering and normalization processing on the historical data time sequence to obtain preprocessed data;
the neural network module 204: acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network;
the demand forecasting module 205: the demand forecasting module is used for carrying out reverse normalization on the normalized region time sequence demand forecasting value, the professional time sequence demand forecasting value and the job time sequence demand forecasting value to obtain expert extraction demand forecasting under the bidding system.
The embodiment of the invention provides a terminal, which comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor is used for executing the computer program so as to realize any one of the expert extraction demand prediction methods based on time series prediction when executing the computer program.
The embodiment of the invention provides a storage medium, which stores an executable computer program, and when the computer program is executed, the expert extraction demand prediction method based on time series prediction is realized.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a sequence of computer program instruction segments for describing the execution of a computer program in a computer device that is capable of performing certain functions.
Those skilled in the art will appreciate that the above description of a computer apparatus is by way of example only and is not intended to be limiting of computer apparatus, and that the apparatus may include more or less components than those described, or some of the components may be combined, or different components may be included, such as input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules/units integrated by the computer device may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, electrical signals, software distribution medium, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. An expert extraction demand prediction method based on time series prediction is characterized by comprising the following steps:
s1, collecting a historical data time sequence of the bidding system historical time period from the database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts;
s2, counting the regional time sequence requirements, professional time sequence requirements and job title time sequence requirements extracted by the experts on the basis of the historical data time sequence;
s3, carrying out wiener filtering and normalization processing on the historical data time sequence to obtain preprocessed data;
s4, acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network;
and S5, performing reverse normalization on the normalized regional time sequence demand predicted value, the professional time sequence demand predicted value and the job title time sequence demand predicted value to obtain expert extraction demand prediction under a bidding system.
2. The expert extraction demand prediction method based on time series prediction as claimed in claim 1, wherein the step S2 is specifically as follows: the bidding system is divided into rectangular grids with set sizes, the historical data time sequence is divided into time blocks with set lengths, the experts are extracted and divided into regional time sequence requirements, professional time sequence requirements and job time sequence requirements, classification and summary statistics of the historical data time sequence are carried out on each time block of each rectangular grid area, and the regional time sequence requirements, the professional time sequence requirements and the job time sequence requirements extracted by the experts corresponding to each time block of each grid area are obtained and used as sample sets.
3. The expert extraction demand prediction method based on time series prediction according to claim 1, wherein in step S3, the wiener filtering process denoises the historical data time series; the wiener filtering calculation formula is as follows:
Figure 587612DEST_PATH_IMAGE001
wherein,
Figure DEST_PATH_IMAGE002
as the initial data, it is,
Figure 35911DEST_PATH_IMAGE003
initial data is added with data subjected to noise removal by wiener filtering,
Figure DEST_PATH_IMAGE004
（0，1]in order to be a noise factor, the noise factor,
Figure 363118DEST_PATH_IMAGE005
random numbers distributed as standard positive symbols
Figure DEST_PATH_IMAGE006
（0，1）。
4. The expert extraction demand prediction method based on time series prediction as claimed in claim 1, wherein in step S3, the normalized calculation formula is:
Figure 289486DEST_PATH_IMAGE007
wherein R isiFor new values of variables after normalization, R
Figure DEST_PATH_IMAGE008
[
Figure 635017DEST_PATH_IMAGE009
]And R is a variable value.
5. The expert extraction demand prediction method based on time series prediction as claimed in claim 1, wherein the step S4 is specifically as follows: dividing the preprocessed sample set into a training set and a testing set; and for each network in the expert extraction condition prediction deep neural network, training and adjusting the structure and parameters of the network through a training set, and performing forward calculation on the test set through each network in the condition prediction deep neural network extracted by each category of experts so as to obtain the average prediction error of the historical data time sequence on the test set through the deep neural network.
6. The expert extraction demand prediction method based on time series prediction as claimed in claim 6, wherein the calculation formula of the average prediction error is:
Figure DEST_PATH_IMAGE010
wherein N represents the number of predicted values obtained by an expert in the historical time period on the test set by extracting the predicted deep neural network,
Figure 254217DEST_PATH_IMAGE011
and extracting a predicted value and a corresponding real time sequence value which are obtained on the test set through the deep neural network for the current expert.
7. The expert extraction demand prediction method based on time series prediction as claimed in claim 1, wherein the denormalization calculation formula is:
Figure DEST_PATH_IMAGE012
wherein R is the inverse normalized variable prediction time series value, and R is the normalized variable prediction time series value.
8. An expert extraction demand prediction device based on time series prediction is characterized by comprising:
a data acquisition module: the data acquisition module is used for acquiring a historical data time sequence of a historical time period of the bidding system from a database; the historical data time sequence comprises a region time sequence, a professional time sequence and a title time sequence which are extracted by experts;
a statistical data module: the statistical data module is used for counting the region time sequence requirement, the professional time sequence requirement and the job title time sequence requirement extracted by the expert based on the historical data time sequence;
a data preprocessing module: the data preprocessing module is used for carrying out wiener filtering and normalization processing on the historical data time sequence to obtain preprocessed data;
a neural network module: acquiring a normalized region time sequence demand predicted value, a professional time sequence demand predicted value and a job title time sequence demand predicted value corresponding to the preprocessed data by using a deep neural network;
a demand forecasting module: the demand forecasting module is used for carrying out reverse normalization on the normalized region time sequence demand forecasting value, the professional time sequence demand forecasting value and the job time sequence demand forecasting value to obtain expert extraction demand forecasting under the bidding system.
9. A terminal comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to execute the method for predicting expert extraction demand based on time series prediction according to any one of claims 1 to 7.
10. A storage medium storing a computer program executable to implement the method for predicting an expert extraction demand based on time-series prediction according to any one of claims 1 to 7.
CN202011453019.3A 2020-12-11 2020-12-11 Expert extraction demand prediction method and device based on time series prediction Pending CN112434882A (en)

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Application publication date: 20210302