CN114862243A - Data processing method and device for assistant decision - Google Patents
Data processing method and device for assistant decision Download PDFInfo
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Abstract
The application discloses a data processing method and device for assisting decision. The method comprises the following steps: in the method, to-be-processed evaluation data are obtained, preprocessing based on data analysis is carried out on the to-be-processed evaluation data to obtain standard evaluation data, and auxiliary decision parameters are determined and processed on the standard evaluation data by adopting a VIKOR algorithm to obtain auxiliary decision parameters; and constructing an auxiliary decision model according to the auxiliary decision parameters, carrying out decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain an output result of the target decision scheme, and outputting the output result of the target decision scheme through a network card. The multiple schemes to be decided are subjected to decision evaluation through a VIKOR algorithm, so that the evaluation decision based on multiple indexes is performed on the multiple schemes to be decided, the accuracy of data corresponding to the decision schemes provided by commercial purchasing is improved, and the server is cooperated to perform data processing of the commercial purchasing auxiliary decision.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a data processing method and apparatus for assisting decision making.
Background
With the continuous development of information technology, the requirements of users on the functions of the server become stricter, and the continuous increase of gigabit network ports leads to the increase of the workload of the server, thereby affecting the working speed of the server. The network card is computer hardware for a computer to communicate on a computer network, and the network card is difficult to perform front-end processing on data received by the server, and the network card is difficult to work with the server in a coordinated manner, so that the data processing is slow.
The inventor finds that the AI chip is added on the basis of the network card, the AI chip can cooperate with the server to perform data processing work, in the selection process of the commercial purchase supplier, the neural network algorithm is used for assisting the commercial purchase decision, the construction of the neural network model has higher requirements on the selection parameter aspect, and the convergence speed aspect has defects, so that the result of local suboptimal convergence is easily caused, and the problem of inaccuracy exists.
Therefore, an effective solution is not provided for the problem that the accuracy of data corresponding to a decision scheme provided for business purchase by the network card added with the AI chip is low.
Disclosure of Invention
The main purpose of the present application is to provide a data processing method and apparatus for aid decision, so as to solve the problem that the accuracy of data corresponding to a decision scheme provided for business procurement by adding a network card of an AI chip is low, implement aid decision for business procurement by using the AI network card, improve the accuracy of data corresponding to the decision scheme provided for business procurement, and cooperate with a server to perform data processing for aid decision for business procurement.
In order to achieve the above object, in a first aspect of the present application, a data processing method for assisting decision making is provided, where the data processing method is applied to a network card integrated with an AI chip, and the data processing method includes:
acquiring to-be-processed evaluation data, wherein the to-be-processed evaluation data are a plurality of index data used for evaluating a plurality of to-be-decided schemes, and any one of the to-be-decided schemes corresponds to a plurality of indexes;
preprocessing the to-be-processed evaluation data based on data analysis to obtain standard evaluation data, wherein the standard evaluation data are evaluation data corresponding to a plurality of evaluation indexes which are converted into the same evaluation data value;
performing auxiliary decision parameter determination processing on the standard evaluation data by adopting a VIKOR algorithm to obtain auxiliary decision parameters;
and constructing an auxiliary decision model according to the auxiliary decision parameters, and performing decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, wherein the target decision scheme output result is output through the network card.
Optionally, performing auxiliary decision parameter determination processing on the standard evaluation data by using a VIKOR algorithm, and obtaining an auxiliary decision parameter includes:
performing first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data, wherein the plurality of first process decision data are decision data of a plurality of schemes to be decided of the first index evaluation, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
performing second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data, wherein the plurality of second process decision data are decision data of a plurality of schemes to be decided of the second index evaluation, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
and performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain the auxiliary decision parameters.
Optionally, performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain the auxiliary decision parameter includes:
acquiring first process decision data and second process decision data, wherein the first process decision data and the second process evaluation data correspond to a first scheme to be decided, and the first scheme to be decided is any one of the plurality of schemes to be decided;
determining weight data corresponding to the first process decision data and the second process decision data to obtain a first weight and a second weight, wherein the first process decision data corresponds to the first weight, and the second process decision data corresponds to the second weight; and
and performing weighting processing on the first process decision data based on the first weight, and performing weighting processing on the second process decision data based on the second weight to obtain the auxiliary decision parameter.
Optionally, the obtaining a first weight and a second weight for determining weight data corresponding to the first process decision data and the second process decision data includes:
identifying evaluation indexes of the first process decision data and the second process decision data to obtain evaluation characteristic data, wherein the evaluation index characteristic data are characteristic data used for representing the evaluation indexes;
matching a decision parameter optimization model corresponding to the evaluation characteristic data in a preset auxiliary decision database;
and performing weighting processing on the first process decision data and the second process decision data according to the decision parameter optimization model to obtain the first weight and the second weight.
Optionally, the pre-processing based on data analysis is performed on the evaluation data to be processed, and obtaining standard evaluation data includes:
identifying data characteristics of the evaluation data to be processed to obtain quantitative evaluation data and qualitative evaluation data, wherein the quantitative evaluation data is used for expressing evaluation index data for quantitatively evaluating the schemes to be decided, and the qualitative evaluation data is used for expressing grading data of experts for the schemes to be decided based on qualitative evaluation indexes;
constructing a process decision matrix according to the quantitative evaluation index data and the qualitative evaluation index data; and
and carrying out standardization processing on the process decision matrix to obtain a standard decision matrix and obtain the standard evaluation data, wherein the standard evaluation data is the standard decision matrix obtained after the standardization processing.
Optionally, constructing an auxiliary decision model according to the auxiliary decision parameter, and performing decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme includes:
performing auxiliary decision prediction processing on the multiple schemes to be decided according to the auxiliary decision model to obtain multiple auxiliary decision data, wherein the multiple auxiliary decision data correspond to the multiple schemes to be decided;
sequencing the plurality of assistant decision data to obtain target assistant decision data, wherein the target assistant decision data is the assistant decision data corresponding to the lowest assistant decision value; and
and acquiring the target decision scheme, wherein the target decision scheme is a scheme to be decided corresponding to the target auxiliary decision data.
According to a second aspect of the present application, there is provided a data processing apparatus for assisting decision making, which is applied to a network card integrated with an AI chip, the data processing apparatus comprising:
the system comprises a data acquisition module, a decision making module and a decision making module, wherein the data acquisition module is used for acquiring evaluation data to be processed, the evaluation data to be processed is a plurality of index data used for evaluating a plurality of schemes to be decided, and any scheme to be decided in the schemes to be decided corresponds to a plurality of indexes;
the preprocessing module is used for preprocessing the evaluation data to be processed based on data analysis to obtain standard evaluation data, wherein the standard evaluation data are evaluation data corresponding to a plurality of evaluation indexes which are converted into the same evaluation data value;
the auxiliary decision parameter determining module is used for performing auxiliary decision parameter determining processing on the standard evaluation data by adopting a VIKOR algorithm to obtain an auxiliary decision parameter;
and the result output module is used for constructing an auxiliary decision model according to the auxiliary decision parameters, and carrying out decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, wherein the target decision scheme output result is output through the network card.
Optionally, the decision assistance parameter determining module comprises:
the first decision module is used for performing first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data, wherein the plurality of first process decision data are decision data of a plurality of schemes to be decided evaluated by the first index, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
a second decision module, configured to perform second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data, where the plurality of second process decision data are decision data of a plurality of schemes to be decided of the second index evaluation, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
and the decision parameter optimization module is used for performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain the auxiliary decision parameters.
According to a third aspect of the present application, there is provided a computer-readable storage medium having stored thereon computer instructions for causing the computer to execute the above-described data processing method for assisting decision-making.
According to a fourth aspect of the present application, an electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the above-mentioned data processing method for assisting decisions.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the method, evaluation data to be processed are obtained, the evaluation data to be processed comprise a plurality of index data used for evaluating a plurality of schemes to be decided, the evaluation data to be processed are preprocessed based on data analysis, evaluation data corresponding to a plurality of evaluation indexes with the same evaluation data value are obtained, standard evaluation data are obtained, and auxiliary decision parameter determining processing is carried out on the standard evaluation data by adopting a VIKOR algorithm, so that auxiliary decision parameters are obtained; and constructing an auxiliary decision model according to the auxiliary decision parameters, carrying out decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain an output result of the target decision scheme, and outputting the output result of the target decision scheme through a network card. Decision evaluation is carried out on the multiple schemes to be decided through a VIKOR algorithm, the multiple schemes to be decided are evaluated and decided based on multiple indexes, decision data of the multiple indexes are optimized, the accuracy of the decision evaluation of the schemes to be decided is improved, the problem that the accuracy of data corresponding to the decision schemes provided for business purchase by network cards added with AI chips is low is solved, business purchase auxiliary decision is carried out through the AI network cards, the accuracy of the data corresponding to the decision schemes provided for business purchase is improved, and data processing of the business purchase auxiliary decision is carried out by a collaboration server.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic flow chart diagram of a data processing method for assisting decision making provided by the present application;
FIG. 2 is a schematic flow chart diagram of a data processing method for assisting decision making provided by the present application;
FIG. 3 is a schematic structural diagram of a data processing apparatus for assisting decision making according to the present application;
fig. 4 is a schematic structural diagram of another data processing apparatus for assisting decision making provided by the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, "connected" may be a fixed connection, a detachable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
When commercial purchasing is carried out, various purchasing schemes are selected, such as the selection of suppliers, the selection of products, the influence on the purchasing scheme selection, various evaluation indexes, the decision on the various evaluation indexes in the various purchasing schemes is needed, and the accuracy of the selection of the decision scheme is improved.
Fig. 1 is a flowchart of a data processing method for assisting decision according to the present application, as shown in fig. 1, the method includes the following steps:
s101: acquiring evaluation data to be processed;
the to-be-processed evaluation data comprises a plurality of index data used for evaluating a plurality of to-be-decided schemes, and any one of the to-be-decided schemes corresponds to a plurality of indexes; when the assistant decision is made, a plurality of data for evaluating the scheme to be decided need to be obtained, a plurality of evaluation indexes exist in each scheme in the process of evaluating each decision scheme, and the characteristic evaluation indexes according to the evaluation indexes comprise quantitative evaluation indexes and qualitative evaluation indexes. Determining the attribute and the dimension of the evaluation index according to the scheme to be decided, for example, if a plurality of schemes to be decided are used for evaluating computer products, the quantitative evaluation index comprises price, CPU, display card, electric quantity and the like, the qualitative evaluation index comprises product quality, heat dissipation effect, brand evaluation and the like, and for the evaluation data of the qualitative evaluation index, the evaluation data is obtained by scoring for experts based on the qualitative evaluation index.
S102: preprocessing the evaluation data to be processed based on data analysis to obtain standard evaluation data;
the standard evaluation data are converted into evaluation data corresponding to a plurality of evaluation indexes with the same evaluation data value;
the evaluation data of the scheme to be decided comprises evaluation data of a first index, evaluation data of a second index and evaluation data of a third index, wherein the evaluation criteria of the first index, the second index and the third index are different, for example, the evaluation criteria of the first index is a value of corresponding price, the evaluation criteria of the second index is a value of corresponding battery capacity, the evaluation criteria of the third index is a value of product quality, and the evaluation data of the first index, the evaluation data of the second index and the evaluation data of the third index have different data dimensions. The method comprises the steps of constructing a first decision matrix according to multi-index evaluation data of a plurality of schemes to be decided in evaluation data to be processed, and carrying out normalized processing on the first decision matrix, wherein the normalized processing method is any one of a range transformation method, a vector normalization method, a proportion transformation method, a linear transformation method and the like, standard evaluation data are obtained after the normalized processing on the first decision matrix, and the standard evaluation data are data of which evaluation values corresponding to the evaluation data of each index of any scheme to be decided fall within [0, 1 ].
In an optional embodiment of the application, data characteristics of evaluation data to be processed are identified to obtain quantitative evaluation data and qualitative evaluation data, wherein the quantitative evaluation data is used for representing evaluation index data of a quantitative evaluation scheme to be decided, and the qualitative evaluation data is score data used for representing experts on a plurality of schemes to be decided based on a qualitative evaluation index; constructing a process decision matrix according to the quantitative evaluation index data and the qualitative evaluation index data; and carrying out standardization processing on the process decision matrix to obtain a standard decision matrix and obtain standard evaluation data, wherein the standard evaluation data is the standard decision matrix obtained after the standardization processing.
S103: performing auxiliary decision parameter determination processing on the standard evaluation data by adopting a VIKOR algorithm to obtain auxiliary decision parameters;
fig. 2 is a flowchart of a data processing method for assisting decision according to the present application, and as shown in fig. 2, the method includes the following steps:
s201: performing first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data;
the plurality of first process decision data are decision data of a plurality of schemes to be decided, which are evaluated by a first index, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
s202: performing second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data;
the plurality of second process decision data are decision data of a plurality of schemes to be decided evaluated by the second index, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
s203: and performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain auxiliary decision parameters.
In an optional embodiment of the present application, a data processing method for assisting decision is provided, in which first process decision data and second process decision data are obtained, where the first process decision data and the second process evaluation data correspond to a first solution to be decided, and the first solution to be decided is any one of a plurality of solutions to be decided; determining weight data corresponding to the first process decision data and the second process decision data to obtain a first weight and a second weight, wherein the first process decision data corresponds to the first weight, and the second process decision data corresponds to the second weight; and performing weighting processing on the first process decision data based on the first weight, and performing weighting processing on the second process decision data based on the second weight to obtain an auxiliary decision parameter.
In another optional embodiment of the present application, a data processing method for assisting decision is provided, in which evaluation indexes of first process decision data and second process decision data are identified to obtain evaluation characteristic data, where the evaluation characteristic data is characteristic data for characterizing the evaluation indexes; matching a decision parameter optimization model corresponding to the evaluation characteristic data in a preset auxiliary decision database; and performing weighting processing on the first process decision data and the second process decision data according to the decision parameter optimization model to obtain a first weight and a second weight.
Calculating the VIKOR value Q of each scheme when the scheme to be decided is calculated by the VIKOR algorithm i ，
Wherein the content of the first and second substances,
wherein the population benefit value Ed i And an individual regret EH i 。Q i Is a comprehensive evaluation value of the ith project, Q i The smaller the value of (A) is, the better the scheme is. x is the number of 1 ,x 2 ∈[0,1]Is a decision coefficient, and x 1 +x 2 1, the individual preference degree is used for representing expert evaluation, and in the prior art, x is selected when calculating the VIKOR value of the scheme to be decided 1 ＝x 2 0.5 results in a lower decision accuracy for each decision scheme.
In the optional embodiment of the application, the decision parameter optimization processing is performed on the plurality of first process decision data and the plurality of second process decision data, the decision coefficient corresponding to each scheme is optimized, modeling is performed by a dispersion maximization method, the model comprises a VIKOR value of the ith scheme,
the VIKOR value of the l-th protocol,
the distance between the two solutions is such that,
The kth expert uses the parameter (x) 1 ,x 2 ) The VIKOR value for the ith decision scheme is Q i k ，
Wherein the group benefit value in the VIKOR value of the kth expert to the ith decision scheme is
And an individual regret value of
After the decision parameter is optimized, the weight data corresponding to different experts is obtained, and the weight data of the kth expert is xi k For the ith decision scheme, the final VIKOR value is
Where p is the total number of experts.
In the embodiment of the application, the auxiliary decision parameters in the VIKOR algorithm processing are optimized, the optimized VIKOR value corresponding to each scheme is obtained by optimizing the corresponding decision coefficient and the weight of expert evaluation according to the schemes to be decided, and a plurality of schemes to be decided are selected according to the optimized VIKOR values, so that the accuracy of scheme decision is improved.
S104: and constructing an auxiliary decision model according to the auxiliary decision parameters, and performing decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, wherein the target decision scheme output result is output through a network card.
Performing auxiliary decision prediction processing on a plurality of schemes to be decided according to an auxiliary decision model to obtain a plurality of auxiliary decision data, wherein the plurality of auxiliary decision data correspond to the plurality of schemes to be decided; sequencing the plurality of auxiliary decision data to obtain target auxiliary decision data, wherein the target auxiliary decision data is the auxiliary decision data corresponding to the lowest auxiliary decision value; and acquiring a target decision scheme, wherein the target decision scheme is a scheme to be decided corresponding to the target auxiliary decision data.
Fig. 3 is a schematic structural diagram of a data processing apparatus for assisting decision, as shown in fig. 3, the apparatus includes:
the data obtaining module 31 is configured to obtain to-be-processed evaluation data, where the to-be-processed evaluation data is multiple index data used for evaluating multiple to-be-decided schemes, and any one of the multiple to-be-decided schemes corresponds to multiple indexes;
the preprocessing module 32 is configured to perform preprocessing based on data analysis on the evaluation data to be processed to obtain standard evaluation data, where the standard evaluation data are evaluation data corresponding to a plurality of evaluation indexes that are converted to have the same evaluation data value;
an assistant decision parameter determining module 33, configured to perform assistant decision parameter determination processing on the standard evaluation data by using a VIKOR algorithm to obtain an assistant decision parameter;
and the result output module 34 is configured to construct an auxiliary decision model according to the auxiliary decision parameters, and perform decision processing on multiple schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, where the target decision scheme output result is output through the network card.
Fig. 4 is a schematic structural diagram of another data processing apparatus for assisting decision making provided by the present application, as shown in fig. 4, the apparatus includes:
the first decision module 41 is configured to perform first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data, where the plurality of first process decision data are decision data of a plurality of schemes to be decided for first index evaluation, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
the second decision module 42 is configured to perform second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data, where the plurality of second process decision data are decision data of a plurality of schemes to be decided for the second index evaluation, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
and a decision parameter optimization module 43, configured to perform decision parameter optimization processing on the multiple first process decision data and the multiple second process decision data to obtain an auxiliary decision parameter.
The specific manner of executing the operations of the units in the above embodiments has been described in detail in the embodiments related to the method, and will not be elaborated herein.
In summary, in the present application, to-be-processed evaluation data is obtained, where the to-be-processed evaluation data includes multiple index data for evaluating multiple to-be-decided schemes, and the to-be-processed evaluation data is subjected to data analysis-based preprocessing to obtain evaluation data corresponding to multiple evaluation indexes having the same evaluation data value, so as to obtain standard evaluation data, and a VIKOR algorithm is used to perform an auxiliary decision parameter determination process on the standard evaluation data to obtain an auxiliary decision parameter; and constructing an auxiliary decision model according to the auxiliary decision parameters, carrying out decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain an output result of the target decision scheme, and outputting the output result of the target decision scheme through a network card. The decision evaluation is carried out on the multiple schemes to be decided through the VIKOR algorithm, the evaluation decision based on multiple indexes is carried out on the multiple schemes to be decided, the decision data of the multiple indexes are optimized, the accuracy of the evaluation decision of the schemes to be decided is improved, the problem that the accuracy of data corresponding to the decision schemes provided for commercial purchase by the network card added with the AI chip is low is solved, the commercial purchase auxiliary decision is carried out through the AI network card, the accuracy of the data corresponding to the decision schemes provided for the commercial purchase is improved, and the data processing of the commercial purchase auxiliary decision is carried out by the cooperation server.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
It will be apparent to those skilled in the art that the various elements or steps of the present application described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of multiple computing devices, or alternatively, may be implemented by program code executable by a computing device, such that the program code may be stored in a memory device and executed by a computing device, or may be implemented by individual integrated circuit modules, or by a plurality of modules or steps included in the program code as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data processing method for assisting decision is applied to a network card integrated with an AI chip, and comprises the following steps:
acquiring to-be-processed evaluation data, wherein the to-be-processed evaluation data comprises a plurality of index data for evaluating a plurality of to-be-decided schemes, and any one of the plurality of to-be-decided schemes corresponds to a plurality of indexes;
preprocessing the to-be-processed evaluation data based on data analysis to obtain standard evaluation data, wherein the standard evaluation data are evaluation data corresponding to a plurality of evaluation indexes which are converted into the same evaluation data value;
performing auxiliary decision parameter determination processing on the standard evaluation data by adopting a VIKOR algorithm to obtain auxiliary decision parameters;
and constructing an auxiliary decision model according to the auxiliary decision parameters, and performing decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, wherein the target decision scheme output result is output through the network card.
2. The data processing method of claim 1, wherein performing an auxiliary decision parameter determination process on the standard evaluation data by using a VIKOR algorithm, and obtaining an auxiliary decision parameter comprises:
performing first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data, wherein the plurality of first process decision data are decision data of a plurality of schemes to be decided of the first index evaluation, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
performing second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data, wherein the plurality of second process decision data are decision data of a plurality of schemes to be decided of the second index evaluation, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
and performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain the auxiliary decision parameters.
3. The data processing method of claim 2, wherein performing decision parameter optimization on the first and second process decision data to obtain the auxiliary decision parameter comprises:
acquiring first process decision data and second process decision data, wherein the first process decision data and the second process evaluation data correspond to a first scheme to be decided, and the first scheme to be decided is any one of the plurality of schemes to be decided;
determining weight data corresponding to the first process decision data and the second process decision data to obtain a first weight and a second weight, wherein the first process decision data corresponds to the first weight, and the second process decision data corresponds to the second weight; and
and performing weighting processing on the first process decision data based on the first weight, and performing weighting processing on the second process decision data based on the second weight to obtain the auxiliary decision parameter.
4. The data processing method of claim 3, wherein obtaining a first weight and a second weight for determining weight data corresponding to the first process decision data and the second process decision data comprises:
identifying evaluation indexes of the first process decision data and the second process decision data to obtain evaluation characteristic data, wherein the evaluation index characteristic data are characteristic data used for representing the evaluation indexes;
matching a decision parameter optimization model corresponding to the evaluation characteristic data in a preset auxiliary decision database;
and performing weighting processing on the first process decision data and the second process decision data according to the decision parameter optimization model to obtain the first weight and the second weight.
5. The data processing method of claim 1, wherein the pre-processing of the evaluation data to be processed based on data analysis to obtain standard evaluation data comprises:
identifying data characteristics of the evaluation data to be processed to obtain quantitative evaluation data and qualitative evaluation data, wherein the quantitative evaluation data is used for expressing evaluation index data for quantitatively evaluating the schemes to be decided, and the qualitative evaluation data is used for expressing grading data of experts for the schemes to be decided based on qualitative evaluation indexes;
constructing a process decision matrix according to the quantitative evaluation index data and the qualitative evaluation index data; and
and carrying out standardization processing on the process decision matrix to obtain a standard decision matrix and obtain the standard evaluation data, wherein the standard evaluation data is the standard decision matrix obtained after the standardization processing.
6. The data processing method according to claim 1, wherein constructing an assistant decision model according to the assistant decision parameters, and performing decision processing on a plurality of schemes to be decided according to the assistant decision model to obtain a target decision scheme comprises:
performing auxiliary decision prediction processing on the multiple schemes to be decided according to the auxiliary decision model to obtain multiple auxiliary decision data, wherein the multiple auxiliary decision data correspond to the multiple schemes to be decided;
sequencing the plurality of auxiliary decision data to obtain target auxiliary decision data, wherein the target auxiliary decision data is the auxiliary decision data corresponding to the lowest auxiliary decision value; and
and acquiring the target decision scheme, wherein the target decision scheme is a scheme to be decided corresponding to the target auxiliary decision data.
7. A data processing device for assisting decision making, which is applied to a network card integrated with an AI chip, the data processing device comprising:
the system comprises a data acquisition module, a decision-making module and a decision-making module, wherein the data acquisition module is used for acquiring to-be-processed evaluation data, the to-be-processed evaluation data are a plurality of index data used for evaluating a plurality of to-be-decided schemes, and any one to-be-decided scheme in the plurality of to-be-decided schemes corresponds to a plurality of indexes;
the preprocessing module is used for preprocessing the evaluation data to be processed based on data analysis to obtain standard evaluation data, wherein the standard evaluation data are evaluation data corresponding to a plurality of evaluation indexes which are converted into the same evaluation data value;
the auxiliary decision parameter determining module is used for performing auxiliary decision parameter determining processing on the standard evaluation data by adopting a VIKOR algorithm to obtain an auxiliary decision parameter;
and the result output module is used for constructing an auxiliary decision model according to the auxiliary decision parameters, and carrying out decision processing on a plurality of schemes to be decided according to the auxiliary decision model to obtain a target decision scheme output result, wherein the target decision scheme output result is output through the network card.
8. The data processing apparatus of claim 7, wherein the decision assistance parameter determining module comprises:
the first decision module is used for performing first index evaluation processing on the standard evaluation data to obtain a plurality of first process decision data, wherein the plurality of first process decision data are decision data of a plurality of schemes to be decided evaluated by the first index, and the plurality of first process decision data correspond to the plurality of schemes to be decided;
a second decision module, configured to perform second index evaluation processing on the standard evaluation data to obtain a plurality of second process decision data, where the plurality of second process decision data are decision data of a plurality of schemes to be decided of the second index evaluation, and the plurality of second process decision data correspond to the plurality of schemes to be decided;
and the decision parameter optimization module is used for performing decision parameter optimization processing on the plurality of first process decision data and the plurality of second process decision data to obtain the auxiliary decision parameters.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the data processing method for assisting decision making according to any one of claims 1 to 6.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the data processing method for assisting decisions of any one of claims 1-6.
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| CN116150222B (en) * | 2022-11-17 | 2023-08-04 | 北京东方通科技股份有限公司 | Auxiliary decision-making method based on big data |
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