CN113256143A - Assessment method for media-melting organization, electronic equipment and readable storage medium - Google Patents
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Abstract
An evaluation method, an electronic device and a readable storage medium for a converged media entity, the method comprising: acquiring basic data of a target media mechanism, and respectively constructing mechanism construction data and fusion effect data according to the basic data; respectively performing weighted evaluation on the two systems by adopting a BP neural network evaluation method to obtain an input score and an output score; and evaluating the efficiency of the input fraction and the output fraction by adopting a DEA model. The evaluation method of the invention constructs two sets of evaluation index systems from the input and output angles to carry out all-round consideration on the media-melting mechanism. And a scientific index weighting method is designed for carrying out index screening and weighting on the two sets of evaluation index systems to obtain a more complete evaluation result. Thereby providing valuable reference opinions and guidance for development and construction of the fusion media. The fusion evaluation method combining subjectivity and objectivity endows each evaluation index with a weight, and overcomes the defects of the existing subjective or objective evaluation method.
Description
Technical Field
The invention belongs to the technical field of a converged media, and particularly relates to an evaluation method for a converged media organization, an electronic device and a readable storage medium.
Background
At present, for the evaluation research of the fusion media, a set of evaluation index system is generally constructed by selecting related evaluation indexes, then weight determination is performed on each evaluation index by using a certain weighting method, and finally an evaluation result is obtained by combining the weight combination with actual data.
In the prior art, comprehensive consideration on a media fusing mechanism in a media fusing environment is lacked, a set of evaluation index system is often constructed, a certain index weighting method is selected to determine the weight of each evaluation index, and finally, a comprehensive evaluation result is obtained by combining actual data. The result lacks of efficiency evaluation on a media-melting mechanism, does not provide a valuable reference guidance function for development of the media-melting mechanism, neglects input and output evaluation aiming at a specific media-melting propagation unit in an evaluation angle, and has the problems of strong subjectivity, lack of scientific evaluation process in index selection and the like in an evaluation method.
Disclosure of Invention
Objects of the invention
The invention aims to provide an assessment method, electronic equipment and a readable storage medium for a fused media mechanism so as to solve the technical problems of strong comprehensive assessment subjectivity and improper index selection in the fused media assessment field in the prior art.
(II) technical scheme
To solve the above problem, a first aspect of the present invention provides an evaluation method for a converged media authority, including: acquiring basic data of a target media organization, wherein the basic data comprises basic data acquired from different channels; respectively constructing mechanism construction data and fusion effect data according to the basic data; weighting and evaluating the mechanism construction data and the fusion effect data respectively by adopting a BP neural network evaluation method to obtain an input score and an output score; and carrying out efficiency evaluation on the input fraction and the output fraction by adopting a DEA model.
Further, the performing efficiency evaluation on the investment score and the output score by using a DEA model includes: and constructing a virtual decision unit by taking the minimum value of the input index and the maximum value of the output index, and adding the virtual decision unit into the DEA model.
Further, the weighting evaluation of the mechanism construction data and the fusion effect data by adopting a BP neural network evaluation method respectively comprises the following steps: acquiring the weight of each piece of data in the organization construction data by adopting a Delphi method; acquiring the weight of each piece of data in the fusion effect data by adopting a Delphi method; and respectively evaluating the mechanism construction data with the weight and the fusion effect data by adopting a BP neural network evaluation method to obtain an input score and an output score.
Further, the weighting evaluation of the mechanism construction data and the fusion effect data by adopting a BP neural network evaluation method further comprises: taking part of the mechanism construction data and the fusion effect data with weights in the same time period as test data; and evaluating the mechanism construction data with the weight except the test data and the fusion effect data by adopting a BP neural network evaluation method.
Further, the evaluating the mechanism construction data and the fusion effect data with weights except the test data by adopting a BP neural network evaluation method comprises the following steps: testing the evaluation value and the partial data, and calculating an error value; and the error value is larger than a second threshold value, a numerical value with the weight value larger than the first threshold value is added in the evaluated input data to be used as the input data for evaluation again, and then the error value is calculated until the calculated error value is smaller than or equal to the second threshold value, so that the input score and the output score are obtained.
Further, the first threshold is a weighted value less than or equal to the weighted value of each index used for evaluation in the previous step, and greater than or equal to the weighted value of the remaining indexes; the range of the second threshold is: 0-0.001.
Further, the proportion of the test data accounts for 5% -10% of the total data proportion.
Further, before the organization construction data and the fusion effect data are respectively weighted and evaluated by adopting a BP neural network evaluation method, the method comprises the following steps: and respectively carrying out non-dimensionalization processing on the mechanism construction data and the fusion effect data.
According to another aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of the above aspects when executing the computer program.
According to a further aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above aspects.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
the assessment method for the media-melting mechanism constructs two sets of assessment index systems from the input and output angles respectively to carry out all-around consideration on the media-melting mechanism. And a scientific index weighting method is designed for carrying out index screening and weighting on the two sets of evaluation index systems to obtain a more complete evaluation result. And on the basis, the efficiency evaluation of the media-fusing mechanism is carried out by utilizing the evaluation result, thereby providing valuable reference opinions and guidance for the development and construction of the media-fusing mechanism. And dividing the evaluation research into evaluation research on dynamic processes of mechanism construction, fusion effect, operation efficiency and the like of the convergence media center according to the practice process and practice result of the convergence media center. Fills the research blank in the evaluation field of the current media center. The fusion evaluation method combining subjectivity and objectivity is adopted to give weights to each evaluation index, and the defects of the existing subjective evaluation method or objective evaluation method are overcome.
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FIG. 1 is a flow diagram of an evaluation method for a converged media authority in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
In the drawings a schematic view of a layer structure according to an embodiment of the invention is shown. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity. The shapes of various regions, layers, and relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, as actually required.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and 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 invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention will be described in more detail below with reference to the accompanying drawings. Like elements in the various figures are denoted by like reference numerals. For purposes of clarity, the various features in the drawings are not necessarily drawn to scale.
AHP, analytical Hierarchy Process, chinese is a delphir's combined Hierarchy analysis method, also known as a multiple-scheme decision method.
The BP Neural Network, Back Propagation Neural Network, Chinese is a Back Propagation Neural Network.
DEA, Data Environment Analysis, Chinese is a Data envelope Analysis method.
FIG. 1 is a flow diagram of an evaluation method for a converged media authority in accordance with an embodiment of the present invention.
As shown in fig. 1, in an embodiment of the present invention, an evaluation method for a converged media authority is provided, which may include: acquiring basic data of a target media organization, wherein the basic data comprises basic data acquired from different channels; respectively constructing mechanism construction data and fusion effect data according to the basic data; weighting and evaluating the mechanism construction data and the fusion effect data respectively by adopting a BP neural network evaluation method to obtain an input score and an output score; and carrying out efficiency evaluation on the input fraction and the output fraction by adopting a DEA model.
The assessment method for the media-melting mechanism constructs two sets of assessment index systems from the input and output angles respectively to carry out all-around consideration on the media-melting mechanism. And a scientific index weighting method is designed for carrying out index screening and weighting on the two sets of evaluation index systems to obtain a more complete evaluation result. And on the basis, the efficiency evaluation of the media-fusing mechanism is carried out by utilizing the evaluation result, thereby providing valuable reference opinions and guidance for the development and construction of the media-fusing mechanism. And dividing the evaluation research into evaluation research on dynamic processes of mechanism construction, fusion effect, operation efficiency and the like of the convergence media center according to the practice process and practice result of the convergence media center. Fills the research blank in the evaluation field of the current media center. The fusion evaluation method combining subjectivity and objectivity is adopted to give weights to each evaluation index, and the defects of the existing subjective evaluation method or objective evaluation method are overcome.
The method comprises the steps of taking a fusion media center as an evaluation object, respectively constructing two sets of evaluation index systems (basic data) for a fusion media mechanism from the input and output angles, namely a mechanism construction evaluation index system (mechanism construction data), and fusing an effect evaluation index system (fusion effect data).
In an alternative embodiment, the institutional construction data may include:
in an optional embodiment, the data of the organization construction in the organization construction data may be obtained from mobile office management platform software of the organization to be evaluated.
In an alternative embodiment, the organization construction data in the organization construction data may be obtained from working software of the organization to be evaluated.
In an alternative embodiment, the tissue construction data in the institution construction data may be obtained from the stapling software of the institution to be evaluated.
In an alternative embodiment, the fusion effect data may include:
in an alternative embodiment, the fusion effectiveness data may be obtained from a public social platform. For example: microblogging, trembling, fast hands, daily top, etc.
In an alternative embodiment, the fusion effectiveness data may be obtained from public social software. For example: WeChat, QQ, etc.
In an alternative embodiment, the organization construction data is divided into a group of data according to the month, and a plurality of groups of the organization construction data are formed.
In an optional embodiment, the fusion effect data is divided into a group of data according to months, and a plurality of groups of the mechanism construction data are formed.
In an alternative embodiment, the performing the efficiency evaluation on the investment score and the yield score by using the DEA model may include: and constructing a virtual decision unit by taking the minimum value of the input index and the maximum value of the output index, and adding the virtual decision unit into the DEA model.
On the basis of input and output of the original DEA model, the minimum value of all input indexes and the maximum value of the output indexes are taken to construct a virtual decision unit to improve the DEA model, and the number of the decision units is changed from the original N to N + 1.
By applying the improved DEA model, M input index combinations of the AHP-BP neural network of N decision units finally obtained during mechanism construction evaluation are used as M input of the DEA model, H input index combinations of the AHP-BP neural network of N decision units finally obtained during fusion effect evaluation are used as H output of the DEA model, minimum values and maximum values of all indexes in input and output are respectively extracted, an N +1 decision unit is constructed, monthly operation efficiency of a fusion media center is calculated, and reference suggestions can be provided for future resource configuration and development emphasis of the fusion media center according to weight combinations of all indexes when the monthly operation efficiency is optimal.
And improving a DEA model according to the characteristics of the converged media center, and constructing a converged media center operation efficiency evaluation model. And acquiring actual data of the fused media center in different time periods, and solving the optimal solution of the operation efficiency of the fused media center by using the model. Therefore, the optimal states of cost, profit and resource allocation are pursued on the premise of reasonable construction investment of the media fusion center, and a reference suggestion is provided for the optimization of the operation efficiency of the media fusion center.
In an alternative embodiment, the weighting and evaluating the organization construction data and the fusion effect data respectively by using a BP neural network evaluation method may include: acquiring the weight of each piece of data in the organization construction data by adopting a Delphi method; acquiring the weight of each piece of data in the fusion effect data by adopting a Delphi method; and respectively evaluating the mechanism construction data with the weight and the fusion effect data by adopting a BP neural network evaluation method to obtain an input score and an output score.
In an optional embodiment, the weighting and evaluating the mechanism construction data and the fusion effect data by using a BP neural network evaluation method may further include: taking part of the mechanism construction data and the fusion effect data with weights in the same time period as test data; and evaluating the mechanism construction data with the weight except the test data and the fusion effect data by adopting a BP neural network evaluation method.
And determining the weight of an index system by using a fusion evaluation method, designing an AHP-BP neural network fusion evaluation method for index determination according to the index numerical characteristics in the two sets of data, and calculating a scoring result with the highest accuracy rate by using the input indexes as less as possible.
In an alternative embodiment, the evaluating the mechanism construction data and the fusion effect data with weights in addition to the test data by using a BP neural network evaluation method may include: testing the evaluation value and the partial data, and calculating an error value; and the error value is larger than a second threshold value, a numerical value with larger weight is added in the evaluated input data to be used as the input data for evaluation again, and then the error value is calculated until the calculated error value is smaller than or equal to the second threshold value, so that the input score and the output score are obtained. The AHP-BP neural network model obtained at the moment can be used for realizing that the scoring effect of experts is simulated by the selected optimal input index combination to obtain the comprehensive evaluation score of the data.
In an alternative embodiment, the evaluating the mechanism construction data and the fusion effect data with weights in addition to the test data by using a BP neural network evaluation method may include: testing the evaluation value and the partial data, and calculating an error value; and the error value is larger than a second threshold value, a numerical value with the weight value larger than the first threshold value is added in the evaluated input data to be used as the input data for evaluation again, and then the error value is calculated until the calculated error value is smaller than or equal to the second threshold value, so that the input score and the output score are obtained.
In an optional embodiment, the first threshold is a weighted value less than or equal to the weighted value of each indicator used for evaluation in the previous step, and greater than or equal to the weighted value of the remaining indicators.
In an alternative embodiment, the range of the second threshold is: 0-0.001.
In an optional embodiment, the proportion of the test data accounts for 5% -10% of the total data proportion.
In an optional embodiment, before performing the weighted evaluation on the mechanism construction data and the fusion effect data respectively by using a BP neural network evaluation method, the method may include: and respectively carrying out non-dimensionalization processing on the mechanism construction data and the fusion effect data.
If 12 months of data are collected to obtain corresponding 12 evaluation results, 10 months of data and corresponding 10 evaluation results are used as a training set, and the rest 2 months of data and corresponding evaluation results are used as a test set. And for input data of the training set, selecting n items of index data with larger weight coefficients as an input set of the BP neural network training model, and taking the obtained corresponding monthly evaluation result as an output data set of the AHP-BP neural network evaluation model. Obtaining parameters of the AHP-BP neural network model through 10 training sets, verifying the error of the model by using the other 2 test sets, if the error is larger, adding an input index to continue training until the error value is within an allowable range, and the AHP-BP neural network model is the best at the moment.
During the validity test of the model, the effectiveness of the model is verified by inputting all index data to directly train the BP neural network and inputting the aging performance and the error performance of the BP neural network which is trained by part of index data with larger weight screened by the AHP method. In general, the AHP-BP neural network model aims to achieve the expert scoring effect that the accuracy is calculated as high as possible by using the input number as few as possible.
On the basis of input and output of the original DEA model, the minimum value of all input indexes and the maximum value of the output indexes are taken to construct a virtual decision unit to improve the DEA model, and the number of the decision units is changed from the original N to N + 1. By applying the improved DEA model, M input index combinations of the AHP-BP neural network of N decision units finally obtained during mechanism construction evaluation are used as M input of the DEA model, H input index combinations of the AHP-BP neural network of N decision units finally obtained during fusion effect evaluation are used as H output of the DEA model, minimum values and maximum values of all indexes in input and output are respectively extracted, an N +1 decision unit is constructed, monthly operation efficiency of a fusion media center is calculated, and reference suggestions can be provided for future resource configuration and development emphasis of the fusion media center according to weight combinations of all indexes when the monthly operation efficiency is optimal. Modified DEA model:
the DEA model can be described in mathematical form as follows:
X0and Y0The input indexes and the output indexes of the current decision unit of the DEA model are respectively, the input indexes and the output indexes in the model are all known quantities of data to be substituted, v and u are respectively the weight coefficients of the input indexes and the output indexes of the current decision unit, and the input indexes and the output indexes in the model are variables to be solved. The essence of the model is that the efficiency evaluation index h of all decision unitsjUnder the constraint that the output indexes are all less than or equal to 1, the ratio of the linear combination of the output indexes and the linear combination of the input indexes of the current decision unit is solved through linear programming, namely the efficiency evaluation index hj0And the corresponding weight coefficient combination.
In another embodiment of the present invention, an electronic device is provided, which may include a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to any one of the above aspects is implemented.
In a further embodiment of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of the above-mentioned solutions.
The invention aims to protect an evaluation method, an electronic device and a readable storage medium for a media-fusing organization, wherein the method comprises the following steps: acquiring basic data of a target media organization, wherein the basic data comprises basic data acquired from different channels; respectively constructing mechanism construction data and fusion effect data according to the basic data; weighting and evaluating the mechanism construction data and the fusion effect data respectively by adopting a BP neural network evaluation method to obtain an input score and an output score; and carrying out efficiency evaluation on the input fraction and the output fraction by adopting a DEA model. The assessment method for the media-melting mechanism constructs two sets of assessment index systems from the input and output angles respectively to carry out all-around consideration on the media-melting mechanism. And a scientific index weighting method is designed for carrying out index screening and weighting on the two sets of evaluation index systems to obtain a more complete evaluation result. And on the basis, the efficiency evaluation of the media-fusing mechanism is carried out by utilizing the evaluation result, thereby providing valuable reference opinions and guidance for the development and construction of the media-fusing mechanism. And dividing the evaluation research into evaluation research on dynamic processes of mechanism construction, fusion effect, operation efficiency and the like of the convergence media center according to the practice process and practice result of the convergence media center. Fills the research blank in the evaluation field of the current media center. The fusion evaluation method combining subjectivity and objectivity is adopted to give weights to each evaluation index, and the defects of the existing subjective evaluation method or objective evaluation method are overcome.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. An evaluation method for a converged media authority, comprising:
acquiring basic data of a target media organization, wherein the basic data comprises basic data acquired from different channels;
respectively constructing mechanism construction data and fusion effect data according to the basic data;
weighting and evaluating the mechanism construction data and the fusion effect data respectively by adopting a BP neural network evaluation method to obtain an input score and an output score;
and carrying out efficiency evaluation on the input fraction and the output fraction by adopting a DEA model.
2. The method for assessing the effectiveness of a media-fusing organization as recited in claim 1, wherein the assessing the effectiveness of the investment score and the yield score using a DEA model comprises:
and constructing a virtual decision unit by taking the minimum value of the input index and the maximum value of the output index, and adding the virtual decision unit into the DEA model.
3. The assessment method for a media-fusing organization according to claim 1 or 2, wherein the weighting assessment of the organization construction data and the fusion effect data by using a BP neural network assessment method comprises:
acquiring the weight of each piece of data in the organization construction data by adopting a Delphi method;
acquiring the weight of each piece of data in the fusion effect data by adopting a Delphi method;
and respectively evaluating the mechanism construction data with the weight and the fusion effect data by adopting a BP neural network evaluation method to obtain an input score and an output score.
4. The method as claimed in claim 3, wherein the weighting evaluation of the organization construction data and the fusion effect data by using a BP neural network evaluation method further comprises:
taking part of the mechanism construction data and the fusion effect data with weights in the same time period as test data;
and evaluating the mechanism construction data with the weight except the test data and the fusion effect data by adopting a BP neural network evaluation method.
5. The assessment method for an organization for syndication according to claim 4, wherein the assessment of the organization construction data and the fusion effect data with weights in addition to the test data by a BP neural network assessment method comprises:
testing the evaluation value and the partial data, and calculating an error value;
and the error value is larger than a second threshold value, a numerical value with the weight value larger than the first threshold value is added in the evaluated input data to be used as the input data for evaluation again, and then the error value is calculated until the calculated error value is smaller than or equal to the second threshold value, so that the input score and the output score are obtained.
6. The evaluation method for a converged media facility of claim 5,
the first threshold value is a weighted value which is less than or equal to the weighted value of each index used for evaluation in the previous step and is greater than or equal to the weighted value of each remaining index;
the range of the second threshold is: 0-0.001.
7. The evaluation method for a converged media facility of claim 5,
the proportion of the test data accounts for 5% -10% of the total data proportion.
8. The assessment method for a media-fusing organization according to any one of claims 1-7, wherein before the organization construction data and the fusion effect data are respectively weighted and assessed by using a BP neural network assessment method, the assessment method comprises:
and respectively carrying out non-dimensionalization processing on the mechanism construction data and the fusion effect data.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any of claims 1-8 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of the preceding claims 1 to 8.
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