CN117829904A - Investment decision prediction method, apparatus, device, storage medium and program product - Google Patents
Investment decision prediction method, apparatus, device, storage medium and program product Download PDFInfo
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Abstract
The present application relates to an investment decision prediction method, apparatus, computer device, storage medium and computer program product. Acquiring target investment decision data of a power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions, an initial investment decision prediction model of the power grid system is determined according to the target investment decision data, the initial investment decision prediction model is trained by utilizing the target investment decision data, a target investment decision prediction model of the power grid system is determined, and an investment decision prediction result of the power grid system is obtained according to a prediction target and the target investment decision prediction model. By adopting the method, the accuracy of the investment decision prediction result of the power grid system can be improved. Meanwhile, compared with the traditional technology, the method and the device have the advantages that the investment decision prediction result is obtained by adopting the target investment decision prediction model, and the intelligent degree is high.
Description
Technical Field
The present application relates to the field of big data and artificial intelligence technology, and in particular, to an investment decision prediction method, apparatus, device, storage medium and program product.
Background
The power grid company needs to analyze the condition to be invested of each component supporting the operation of the power grid system to obtain an investment decision of the power grid system, and an investment plan is arranged according to the investment decision so as to strengthen power grid interconnection and intercommunication, solve the problem of large-scale grid connection of renewable energy sources and reduce unnecessary resource waste. The components supporting the operation of the power grid system comprise power generation facilities, a power transmission and distribution network, energy storage facilities, intelligent power grid technology and the like.
In the conventional technology, collected power grid data is usually analyzed manually according to past investment decision experience of a power grid system, so that future investment decision prediction results of the power grid system are obtained.
However, the accuracy of the investment decision prediction results obtained by the above method is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an investment decision prediction method, apparatus, device, storage medium, and program product that can improve the accuracy of investment decision prediction results for a power grid system.
In a first aspect, the present application provides an investment decision prediction method comprising:
acquiring target investment decision data of the power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training the initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In one embodiment, the acquiring the target investment decision data of the power grid system according to the historical investment decision data and the preset condition of the power grid system includes:
determining initial investment decision data of the power grid system according to the historical investment decision data and the preset condition;
processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data;
and acquiring the target investment decision data based on the intermediate investment decision data and the data quality index.
In one embodiment, the preset data integration rule includes a preset data format rule, and the processing the initial investment decision data according to the preset data integration rule to obtain intermediate investment decision data includes:
performing format conversion on the initial investment decision data according to the preset data format rule to obtain first investment decision data;
the intermediate investment decision data is determined based on the first investment decision data.
In one embodiment, the preset data integration rule further includes a preset data conversion rule and a preset field mapping rule, and the processing the initial investment decision data according to the preset data integration rule to obtain intermediate investment decision data includes:
determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
processing the second investment decision data by utilizing the preset data conversion rule to obtain fourth investment decision data;
processing the third investment decision data by utilizing the preset field mapping rule to obtain fifth investment decision data;
Determining the intermediate investment decision data based on the first investment decision data, the fourth investment decision data and the fifth investment decision data.
In one embodiment, the obtaining the target investment decision data based on the intermediate investment decision data and the data quality indicator comprises:
cleaning the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data;
carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data;
and screening the seventh investment decision data by utilizing the data quality index to obtain the target investment decision data.
In one embodiment, the determining an initial investment decision prediction model of the grid system based on the target investment decision data comprises:
extracting features of the target investment decision data, and determining key features of the target investment decision data;
modeling the target investment decision data and determining a target mode of the target investment decision data;
and constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
In a second aspect, the present application also provides an investment decision prediction apparatus, comprising:
the acquisition module is used for acquiring target investment decision data of the power grid system according to the historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
the first determining module is used for determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
the second determining module is used for training the initial investment decision prediction model by utilizing the target investment decision data and determining a target investment decision prediction model of the power grid system;
and the third determining module is used for obtaining the investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring target investment decision data of the power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
Determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training the initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring target investment decision data of the power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training the initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring target investment decision data of the power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training the initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
The investment decision prediction method, the device, the computer equipment, the storage medium and the computer program product acquire target investment decision data of the power grid system according to the historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions, an initial investment decision prediction model of the power grid system is determined according to the target investment decision data, the initial investment decision prediction model is trained by utilizing the target investment decision data, a target investment decision prediction model of the power grid system is determined, and an investment decision prediction result of the power grid system is obtained according to a prediction target and the target investment decision prediction model. In the conventional technology, collected power grid data is usually analyzed manually according to past investment decision experience of a power grid system, so that future investment decision prediction results of the power grid system are obtained. However, the accuracy of the investment decision prediction results obtained is low. In the embodiment of the application, the accuracy of the initial investment decision prediction model can be improved by acquiring a large amount of target investment decision data and constructing the initial investment decision prediction model according to the target investment decision data, and further, the accuracy of the target investment decision prediction model obtained by using the initial investment decision prediction model is improved. Meanwhile, compared with the traditional technology, the method and the device have the advantages that the investment decision prediction result is obtained by adopting the target investment decision prediction model, and the intelligent degree is high.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a diagram of an application environment for an investment decision prediction method in one embodiment;
FIG. 2 is a flow diagram of an investment decision prediction method in one embodiment;
FIG. 3 is a flow chart of an investment decision prediction method in another embodiment;
FIG. 4 is a flow chart of an investment decision prediction method in another embodiment;
FIG. 5 is a flow chart of an investment decision prediction method in another embodiment;
FIG. 6 is a flow chart of an investment decision prediction method in another embodiment;
FIG. 7 is a flow chart of an investment decision prediction method in another embodiment;
FIG. 8 is a schematic diagram of investment decision prediction provided by one embodiment;
FIG. 9 is a block diagram of an investment decision prediction apparatus in one embodiment;
Fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The investment decision prediction method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may send the historical investment decision data and the preset condition to the server 104, and the server 104 may obtain the target investment decision data of the power grid system according to the historical investment decision data and the preset condition, so as to obtain an investment decision prediction result of the power grid system subsequently. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, which may be smart watches, smart bracelets, headsets, etc. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers. The terminal with the calculation function meeting the prediction requirement can also be directly used for acquiring the target investment decision data of the power grid system according to the historical investment decision data and the preset condition, so that the investment decision prediction result of the power grid system is obtained later.
In an exemplary embodiment, as shown in fig. 2, there is provided an investment decision prediction method, where the embodiment is illustrated by applying the method to a server, it is understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server, including:
s201, acquiring target investment decision data of a power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions.
The preset condition can be used as an acquisition condition for initial investment decision data. The preset conditions may include index conditions, technical conditions, market conditions, environmental conditions, economic conditions, social conditions, competitive conditions, and the like.
In this embodiment, the investment decision data corresponding to the preset condition may be obtained according to the preset condition, and the investment decision data corresponding to the preset condition and the historical investment decision data are used as the initial investment decision data. That is, the initial investment decision data may include investment decision data and historical investment decision data corresponding to the preset condition. The initial investment decision data can be integrated to obtain integrated initial investment decision data, and then the integrated initial investment decision data is cleaned to obtain target investment decision data of the power grid system. It should be noted that the initial investment decision data may be derived from different systems and organizations, may be structured data, may be unstructured data, and may be various data related to the investment decision of the power grid, including but not limited to investment policy, investment direction, key engineering or special task related data.
For example, the index data may be acquired according to the index condition, the technical condition data may be acquired according to the technical condition, the market data may be acquired according to the market condition, the environmental data may be acquired according to the environmental condition, the economic data may be acquired according to the economic condition, the social condition data may be acquired according to the social condition, the competitive condition data may be acquired according to the competitive condition, the acquired index data, the technical condition data, the market data, the environmental data, the economic data, the social condition data, the competitive condition data and the historical investment decision data may be used as initial investment decision data, and the integration and the cleaning may be performed to obtain the target investment decision data.
S202, determining an initial investment decision prediction model of the power grid system according to the target investment decision data.
In the embodiment of the application, the big data analysis and/or mining technology can be utilized to analyze and/or mine the target investment decision data so as to perform at least one of preprocessing, feature extraction and pattern recognition on the target investment decision data to obtain a processing result, and find out the association rule, the prediction trend and the evaluation risk condition among the target investment decision data, so that an initial investment decision prediction model of the power grid system is constructed by utilizing the processing result.
For example, data such as annual investment reserves, investment scale correlation coefficients, etc. may be analyzed and/or mined to obtain the treatment results. The investment scale correlation coefficient may include an investment yield, a market index, an economic index, an investment risk, and the like.
It should be noted that, the preprocessing may include data dimension reduction, and performing Principal Component Analysis (PCA) or feature selection on the high-dimensional data, so as to reduce complexity of the data and improve efficiency of the model. The preprocessing may also include labeling and classifying the data for subsequent model training and analysis, which may include grouping or tagging the data. The preprocessing may also include data segmentation, randomly segmenting the data set into a training set and a testing set for training and verification of the model, and the like. Preprocessing may also include data cleansing and processing, data integration, data transformation, anomaly detection processing, and the like. In the examples provided herein, the pretreatment method is not limited.
S203, training an initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system.
In the embodiment of the application, each target investment decision data can be used as a sample set, and sample data in the sample set is input into an initial investment decision prediction model to obtain a first training result. Comparing the first training result with a real result corresponding to the sample data, if the loss value of the first predicted result and the real result is larger than a preset result threshold value, adjusting parameters in the initial investment decision prediction model, and inputting the other sample data in the sample set into the adjusted prediction model to obtain a second predicted result. Referring to the above, if the loss values of the second predicted result and the real result are greater than the preset result threshold, continuing to adjust the parameters in the predicted model; and if the loss values of the second predicted result and the real result are not greater than the preset result threshold value, ending training, and determining the prediction model at the end as an intermediate investment decision prediction model.
Partial data in the target investment decision data can be obtained and used as verification data, and a plurality of verification data are input into the intermediate investment decision prediction model to obtain a plurality of verification results; the intermediate investment decision prediction model may be evaluated according to a plurality of verification results, so as to adjust model parameters or model structures according to the evaluation results, and obtain a target investment decision prediction model. And the intermediate investment decision prediction model can be evaluated based on the preset index and a plurality of verification results to obtain a target investment decision prediction model. The preset index may include accuracy of the model, calculation speed of the model, and the like. And if the accuracy of the model is smaller than a preset accuracy threshold, or the calculation speed of the model is smaller than a preset speed threshold, the model parameters or the model structure can be adjusted.
S204, obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
The prediction targets may include previous year investment completion conditions, regional power equipment conservation amount differences, company investment strategies and investment directions, important projects or special tasks, current year prediction investment scales and the like.
In the embodiment of the application, according to at least one of prediction targets such as the previous year investment completion condition, regional power equipment conservation amount difference, company investment strategy and investment direction, key engineering or special task, current year prediction investment scale and the like, test data corresponding to target investment decision data can be input into a target investment decision prediction model, and the target investment decision prediction model outputs an investment decision prediction result of a power grid system. The investment decision prediction result of the power grid system can be a medium-risk investment scheme, a high-risk investment scheme and a low-risk investment scheme under different prediction targets. Wherein a high scenario may comprise a high growth potential target, a medium scenario may be a robust growth target, and a low scenario may be a steady yield target.
The investment decision prediction method, the device, the equipment, the storage medium and the program product acquire target investment decision data of the power grid system according to the historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions, an initial investment decision prediction model of the power grid system is determined according to target investment decision data, the initial investment decision prediction model is trained by utilizing the target investment decision data, a target investment decision prediction model of the power grid system is determined, and an investment decision prediction result of the power grid system is obtained according to the prediction targets and the target investment decision prediction model. In the conventional technology, collected power grid data is usually analyzed manually according to past investment decision experience of a power grid system, so that future investment decision prediction results of the power grid system are obtained. However, the accuracy of the investment decision prediction results obtained is low. In the embodiment of the application, the accuracy of the initial investment decision prediction model can be improved by acquiring a large amount of target investment decision data and constructing the initial investment decision prediction model according to the target investment decision data, and further, the accuracy of the target investment decision prediction model obtained by using the initial investment decision prediction model is improved. Meanwhile, compared with the traditional technology, the method and the device have the advantages that the investment decision prediction result is obtained by adopting the target investment decision prediction model, and the intelligent degree is high.
In an exemplary embodiment, as shown in fig. 3, this embodiment relates to a possible implementation manner of obtaining target investment decision data of a power grid system according to historical investment decision data and preset conditions of the power grid system, and S201 includes:
s301, determining initial investment decision data of the power grid system according to historical investment decision data and preset conditions.
In the embodiment of the application, the investment decision data corresponding to the preset condition can be collected according to the preset condition, and the investment decision data and the historical investment decision data corresponding to the preset condition are used as initial investment decision data. The historical investment decision data can also be screened according to the investment decision prediction requirement of the current power grid system, and the screened historical investment decision data and the investment decision data corresponding to the preset condition are used as the initial investment decision data of the power grid system.
S302, processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data.
Optionally, the initial investment decision data is subjected to standardization processing according to a standardization rule in the preset data integration rule, so as to obtain intermediate investment decision data.
Optionally, as shown in fig. 4, in the case that the preset data integration rule includes a preset data format rule, processing the initial investment decision data according to the preset data integration rule to obtain intermediate investment decision data may specifically include the following embodiments:
s401, performing format conversion on the initial investment decision data according to a preset data format rule to obtain first investment decision data.
For example, if the preset data format rule is a binary format, format conversion may be performed on data in a non-binary format in the initial investment decision data to obtain the first investment decision data. For example, the initial investment decision data in the form of pictures and text data is converted into binary data to obtain first investment decision data. The preset data format rule is not limited in the embodiment of the present application.
S402, determining intermediate investment decision data according to the first investment decision data.
Alternatively, the first investment decision data may be taken as intermediate investment decision data.
Optionally, the decision data to be subjected to data conversion in the first investment decision data may be subjected to data conversion, and then the decision data subjected to data conversion may be subjected to field mapping, so as to obtain intermediate investment decision data.
In this embodiment, format conversion is performed on the initial investment decision data according to a preset data format rule, and the format of the initial investment decision data is uniform, so that convenience and accuracy in subsequent determination of intermediate investment decision data can be improved.
Optionally, in the case that the preset data integration rule further includes a preset data conversion rule and a preset field mapping rule, processing the initial investment decision data according to the preset data integration rule to obtain intermediate investment decision data, which may specifically include the following embodiments:
s501, determining second investment decision data and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be field mapped.
For example, decision data to be data converted and decision data to be field mapped may be determined from the first investment decision data.
S502, processing the second investment decision data by utilizing a preset data conversion rule to obtain fourth investment decision data.
For example, the decision data to be subjected to data conversion may be processed by using a preset data conversion rule, so as to obtain fourth investment decision data. For example, if the preset data conversion rule is uppercase-uppercase, and the second investment decision data is uppercase-uppercase data, uppercase-lowercase processing may be performed on the second investment decision data to obtain fourth investment decision data, where the fourth investment decision data is lowercase. The preset data conversion rule is not limited in the embodiment of the present application.
S503, processing the third investment decision data by utilizing a preset field mapping rule to obtain fifth investment decision data.
Illustratively, the decision data to be field mapped may be processed using a preset field mapping rule to obtain fifth investment decision data. For example, if the preset field mapping rule is that the ID field is mapped to the idcode field, the mapping process may be performed on the third investment decision data to obtain fifth investment decision data. The preset field mapping rule is not limited in the embodiment of the present application.
S504, determining intermediate investment decision data according to the first investment decision data, the fourth investment decision data and the fifth investment decision data.
For example, the first investment decision data, which is not processed, and the fourth investment decision data and the fifth investment decision data may be taken as intermediate investment decision data.
In this embodiment, a plurality of preset data integration rules are provided, and the preset data integration rules can be determined for the historical investment decision data according to the investment decision prediction requirement of the current power grid system, so that the flexibility of acquiring the intermediate investment decision data can be improved.
S303, acquiring target investment decision data based on the intermediate investment decision data and the data quality index.
Optionally, the intermediate investment decision data can be standardized directly according to a preset standardization rule, and then the standardized intermediate investment decision data is screened according to a data quality index to obtain target investment decision data.
Alternatively, as shown in fig. 6, based on the intermediate investment decision data and the data quality index, obtaining the target investment decision data may include the following embodiments:
s601, cleaning the intermediate investment decision data by using a preset cleaning rule to obtain the intermediate investment decision data.
For example, the intermediate investment decision data may have missing values, repeated data, error data, etc., and the intermediate investment decision data is cleaned using a preset cleaning rule to obtain sixth investment decision data. To ensure the accuracy and integrity of the intermediate investment decision data. The preset cleaning rules comprise removing repeated data, filling missing values, correcting data errors and the like. The embodiment of the application does not limit the preset cleaning rule.
S602, carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data.
Illustratively, the cleaned intermediate investment decision data, i.e., the intermediate investment decision data, may be normalized to obtain seventh investment decision data to accommodate the need for subsequent analysis and construction of the initial investment decision prediction model.
S603, screening the seventh investment decision data by utilizing the data quality index to obtain target investment decision data.
The data quality index can be established according to the investment decision prediction requirement of the current power grid system, and the seventh investment decision data is screened by utilizing the data quality index to obtain target investment decision data so as to ensure that the quality of the data meets the requirement. For example, evaluating the quality of the seventh investment decision data through a data quality index, and eliminating the seventh investment decision data with poor quality; or an abnormality detection method that eliminates abnormal data in the seventh investment decision data.
In this embodiment, the intermediate investment decision data is cleaned by using a preset cleaning rule to obtain intermediate investment decision data, the sixth investment decision data is subjected to standardization processing to obtain seventh investment decision data, and the seventh investment decision data is screened by using a data quality index to obtain target investment decision data. The quality of the investment decision data can be improved, so that the accuracy of the investment decision prediction result of the power grid system can be improved.
In an exemplary embodiment, as shown in fig. 7, this embodiment relates to a possible implementation of an initial investment decision prediction model of a power grid system according to target investment decision data, where S202 includes:
S701, extracting features of the target investment decision data, and determining key features of the target investment decision data.
For example, feature extraction may be performed on the target investment decision data to obtain key features of the target investment decision data. Key features may include indicators and factors of various aspects of financial data, market data, industry data, policy data, etc., which may include revenue growth rates, profit margins, resource fluctuations in market data, trading volume, etc.
S702, modeling the target investment decision data and determining a target mode of the target investment decision data.
The target mode may be a mode corresponding to a similar trend, a mode corresponding to a similar rule, or a mode corresponding to an abnormal situation.
For example, the target investment decision data can be analytically modeled, and target modes under different market conditions can be obtained through mode identification, wherein the target modes can be investment modes and risk modes. The target investment decision data can be analyzed and modeled, and the target trend of the target investment decision data with regular properties can be obtained through trend identification. The target trends and target patterns may provide references for investment decisions.
For example, pattern recognition may be determining investment patterns under different market conditions by cluster analysis, using time series analysis to find trends and periodicity of the market, using regression analysis to find the relationship between features and return on investment, etc.
S703, constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
Optionally, an initial investment decision prediction model of the power grid system may also be constructed according to key features of the target investment decision data, a target pattern of the target investment decision data, and a target trend.
In this embodiment, by extracting features of the target investment decision data, determining key features of the target investment decision data, modeling the target investment decision data, determining a target mode of the target investment decision data, and constructing an initial investment decision prediction model of the power grid system according to the key features of the target investment decision data and the target mode of the target investment decision data. Because the initial investment decision prediction model of the power grid system is constructed after the target investment decision data is analyzed according to the requirements, the accuracy and the precision of the initial investment decision prediction model can be improved.
In one embodiment, as shown in FIG. 8, FIG. 8 is a schematic diagram of investment decision prediction provided by one embodiment. According to at least one of the prediction targets of the previous year investment completion situation, the regional power equipment conservation amount difference, the company investment strategy and investment direction, the key engineering or special task, the annual prediction investment scale and the like, the test data corresponding to the target investment decision data are input into a target investment decision prediction model, and the target investment decision prediction model outputs a medium risk investment scheme, a high risk investment scheme and a low risk investment scheme corresponding to each project in the power grid system. Wherein a high scenario may comprise a high growth potential target, a medium scenario may be a robust growth target, and a low scenario may be a steady yield target.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an investment decision prediction device for realizing the above-mentioned investment decision prediction method. The implementation of the solution provided by the apparatus is similar to that described in the above method, so the specific limitations in one or more embodiments of the investment decision prediction apparatus provided below may be referred to above as limitations of the investment decision prediction method, and will not be described in detail herein.
In one exemplary embodiment, as shown in FIG. 9, there is provided an investment decision prediction apparatus 900 comprising: an acquisition module 901, a first determination module 902, a second determination module 903, and a third determination module 904, wherein:
the acquisition module 901 is configured to acquire target investment decision data of the power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions.
A first determining module 902 is configured to determine an initial investment decision prediction model of the grid system based on the target investment decision data.
A second determining module 903, configured to train the initial investment decision prediction model with the target investment decision data, and determine a target investment decision prediction model of the power grid system.
And the third determining module 904 is configured to obtain an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In one exemplary embodiment, the third determination module 904 includes:
and the first determining unit is used for determining initial investment decision data of the power grid system according to the historical investment decision data and preset conditions.
And the second determining unit is used for processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data.
And the acquisition unit is used for acquiring the target investment decision data based on the intermediate investment decision data and the data quality index.
In an exemplary embodiment, in case that the preset data integration rule includes a preset data format rule, the second determining unit includes:
the first determining sub-module is used for carrying out format conversion on the initial investment decision data according to a preset data format rule to obtain first investment decision data.
And the second determining sub-module is used for determining intermediate investment decision data according to the first investment decision data.
In an exemplary embodiment, in case that the preset data integration rule further includes a preset data conversion rule and a preset field mapping rule, the second determining unit includes:
A third determination sub-module for determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
a fourth determining sub-module, configured to process the second investment decision data by using a preset data conversion rule to obtain fourth investment decision data;
a fifth determining submodule, configured to process the third investment decision data by using a preset field mapping rule to obtain fifth investment decision data;
a sixth determination sub-module for determining intermediate investment decision data based on the first investment decision data, the fourth investment decision data, and the fifth investment decision data.
In an exemplary embodiment, the obtaining unit is specifically configured to clean the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data; carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data; and screening the seventh investment decision data by utilizing the data quality index to obtain target investment decision data.
In one exemplary embodiment, the first determining module 902 includes:
and the third determining unit is used for extracting the characteristics of the target investment decision data and determining the key characteristics of the target investment decision data.
And the fourth determining unit is used for modeling the target investment decision data and determining a target mode of the target investment decision data.
And the construction unit is used for constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
The various modules in the investment decision prediction apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 10. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an investment decision prediction method.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring target investment decision data of the power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training an initial investment decision prediction model by utilizing target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining initial investment decision data of the power grid system according to the historical investment decision data and preset conditions;
processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data;
and acquiring target investment decision data based on the intermediate investment decision data and the data quality index.
In one embodiment, the processor when executing the computer program further performs the steps of:
performing format conversion on the initial investment decision data according to a preset data format rule to obtain first investment decision data;
intermediate investment decision data is determined from the first investment decision data.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
processing the second investment decision data by utilizing a preset data conversion rule to obtain fourth investment decision data;
Processing the third investment decision data by using a preset field mapping rule to obtain fifth investment decision data;
intermediate investment decision data is determined based on the first investment decision data, the fourth investment decision data, and the fifth investment decision data.
In one embodiment, the processor when executing the computer program further performs the steps of:
cleaning the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data;
carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data;
and screening the seventh investment decision data by utilizing the data quality index to obtain target investment decision data.
In one embodiment, the processor when executing the computer program further performs the steps of:
extracting features of the target investment decision data, and determining key features of the target investment decision data;
modeling the target investment decision data and determining a target mode of the target investment decision data;
and constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Acquiring target investment decision data of the power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training an initial investment decision prediction model by utilizing target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining initial investment decision data of the power grid system according to the historical investment decision data and preset conditions;
processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data;
and acquiring target investment decision data based on the intermediate investment decision data and the data quality index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing format conversion on the initial investment decision data according to a preset data format rule to obtain first investment decision data;
Intermediate investment decision data is determined from the first investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
processing the second investment decision data by utilizing a preset data conversion rule to obtain fourth investment decision data;
processing the third investment decision data by using a preset field mapping rule to obtain fifth investment decision data;
intermediate investment decision data is determined based on the first investment decision data, the fourth investment decision data, and the fifth investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
cleaning the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data;
carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data;
and screening the seventh investment decision data by utilizing the data quality index to obtain target investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting features of the target investment decision data, and determining key features of the target investment decision data;
modeling the target investment decision data and determining a target mode of the target investment decision data;
and constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring target investment decision data of the power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
training an initial investment decision prediction model by utilizing target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining initial investment decision data of the power grid system according to the historical investment decision data and preset conditions;
processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data;
and acquiring target investment decision data based on the intermediate investment decision data and the data quality index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing format conversion on the initial investment decision data according to a preset data format rule to obtain first investment decision data;
intermediate investment decision data is determined from the first investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
processing the second investment decision data by utilizing a preset data conversion rule to obtain fourth investment decision data;
Processing the third investment decision data by using a preset field mapping rule to obtain fifth investment decision data;
intermediate investment decision data is determined based on the first investment decision data, the fourth investment decision data, and the fifth investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
cleaning the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data;
carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data;
and screening the seventh investment decision data by utilizing the data quality index to obtain target investment decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting features of the target investment decision data, and determining key features of the target investment decision data;
modeling the target investment decision data and determining a target mode of the target investment decision data;
and constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of investment decision prediction, the method comprising:
acquiring target investment decision data of a power grid system according to historical investment decision data and preset conditions of the power grid system; the preset conditions comprise index conditions, technical conditions and market conditions;
determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
Training the initial investment decision prediction model by utilizing the target investment decision data, and determining a target investment decision prediction model of the power grid system;
and obtaining an investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
2. The method of claim 1, wherein the obtaining the target investment decision data of the grid system based on the historical investment decision data of the grid system and the preset condition comprises:
determining initial investment decision data of the power grid system according to the historical investment decision data and the preset conditions;
processing the initial investment decision data according to a preset data integration rule to obtain intermediate investment decision data;
and acquiring the target investment decision data based on the intermediate investment decision data and the data quality index.
3. The method of claim 2, wherein the predetermined data integration rule comprises a predetermined data format rule, and wherein the processing the initial investment decision data according to the predetermined data integration rule to obtain intermediate investment decision data comprises:
Performing format conversion on the initial investment decision data according to the preset data format rule to obtain first investment decision data;
determining the intermediate investment decision data from the first investment decision data.
4. A method according to claim 3, wherein the preset data integration rules further comprise preset data conversion rules and preset field mapping rules, and the processing the initial investment decision data according to the preset data integration rules to obtain intermediate investment decision data comprises:
determining second and third investment decision data from the first investment decision data; the second investment decision data is decision data to be subjected to data conversion; the third investment decision data is decision data to be subjected to field mapping;
processing the second investment decision data by utilizing the preset data conversion rule to obtain fourth investment decision data;
processing the third investment decision data by utilizing the preset field mapping rule to obtain fifth investment decision data;
determining the intermediate investment decision data based on the first investment decision data, the fourth investment decision data, and the fifth investment decision data.
5. The method of claim 2, wherein the obtaining the target investment decision data based on the intermediate investment decision data and a data quality indicator comprises:
cleaning the intermediate investment decision data by using a preset cleaning rule to obtain sixth investment decision data;
carrying out standardization processing on the sixth investment decision data to obtain seventh investment decision data;
and screening the seventh investment decision data by utilizing the data quality index to obtain the target investment decision data.
6. The method according to any one of claims 1-5, wherein said determining an initial investment decision prediction model of the grid system from the target investment decision data comprises:
extracting features of the target investment decision data, and determining key features of the target investment decision data;
modeling the target investment decision data and determining a target mode of the target investment decision data;
and constructing an initial investment decision prediction model of the power grid system according to the key characteristics of the target investment decision data and the target mode of the target investment decision data.
7. An investment decision prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring target investment decision data of the power grid system according to historical investment decision data of the power grid system and preset conditions; the preset conditions comprise index conditions, technical conditions and market conditions;
the first determining module is used for determining an initial investment decision prediction model of the power grid system according to the target investment decision data;
the second determining module is used for training the initial investment decision prediction model by utilizing the target investment decision data and determining a target investment decision prediction model of the power grid system;
and the third determining module is used for obtaining the investment decision prediction result of the power grid system according to the prediction target and the target investment decision prediction model.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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