CN116663812A - Evaluation method and device for data sharing effect in transportation planning industry - Google Patents
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Abstract
The invention provides a method and a device for evaluating a data sharing effect in the traffic planning industry, wherein the method comprises the following steps: based on a pre-established target index system, obtaining sharing effect scoring data corresponding to the data sharing work to be evaluated; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level; carrying out data preprocessing on the sharing effect scoring data, and determining a target sharing effect score corresponding to each target index; and determining a sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index. The invention can truly, accurately and comprehensively evaluate the data sharing effect.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for evaluating a data sharing effect in the transportation planning industry.
Background
At present, data sharing is mainly based on networking or integration of various types of data developed by related departments, and the aimed sharing effect evaluation is mainly based on the data sharing effect evaluation of the related departments, so that scientific research data sharing mechanisms or systems aimed at a certain industry (such as the comprehensive transportation planning field) are fewer, and therefore, related research or application of how to evaluate the data sharing effect in the industry is insufficient. How to evaluate the data sharing effect truly, accurately and comprehensively is a difficult problem in the data sharing work, and no complete evaluation method or system exists yet.
Disclosure of Invention
Therefore, the invention aims to provide a method and a device for evaluating the data sharing effect in the transportation planning industry, which can evaluate the data sharing effect truly, accurately and comprehensively.
In a first aspect, an embodiment of the present invention provides a method for evaluating a data sharing effect in a transportation planning industry, including: based on a pre-established target index system, obtaining sharing effect scoring data corresponding to the data sharing work to be evaluated; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level; performing data preprocessing on the sharing effect scoring data, and determining a target sharing effect score corresponding to each target index; and determining a sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index.
In one embodiment, before obtaining the sharing effect scoring data corresponding to the data sharing job to be evaluated based on the pre-established target index system, the method further includes: acquiring a pre-established initial index system and an index scoring result corresponding to the initial index system; the initial index system comprises a multi-level initial index set, the index scoring result comprises at least one single index importance score corresponding to each initial index in the initial index set of the designated level, and the initial index set of the designated level is a final initial index set in the initial index system; for each initial index, judging whether the initial index meets a preset index condition or not based on the index scoring result; if so, determining the initial index as a target index, and constructing a target index system based on each target index.
In one embodiment, determining whether the initial indicator satisfies a preset indicator condition based on the indicator scoring result includes: based on the index scoring result, respectively determining an importance weighted average value, a variation coefficient and membership corresponding to the initial index; and if the importance weighted average is greater than a preset importance threshold, the variation coefficient is smaller than a preset variation coefficient threshold, and the membership is greater than a preset membership threshold, determining that the initial index meets a preset index condition.
In one embodiment, based on the index scoring result, determining the weighted average of importance, the variation coefficient and the membership corresponding to the initial index respectively includes: determining an importance weighted average corresponding to the initial index based on each single index importance score corresponding to the initial index and a first number of the single index importance scores; and determining a scoring standard deviation corresponding to the initial index based on each single index importance score corresponding to the initial index, and determining the ratio of the scoring standard deviation to the importance weighted average corresponding to the initial index as a variation coefficient corresponding to the initial index; and determining the total number of the single-index importance scores corresponding to the initial index and the second number of the single-index importance scores larger than a preset scoring threshold, and determining the ratio of the second number to the total number as the membership corresponding to the initial index.
In one embodiment, the index scoring result further includes a multi-index importance score between any two initial indexes in each level of initial index set; before determining the sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index, the method further comprises: for each level of target index set, constructing an importance scoring matrix corresponding to the level of target index set; the system comprises a level target index set, an importance degree scoring matrix, a target index set and a target index set, wherein the line and the column of the importance degree scoring matrix are all target indexes contained in the level target index set, and the elements of the importance degree scoring matrix are used for representing multi-index importance degree scoring between the two target indexes; consistency verification is carried out on the importance scoring matrix; if the importance scoring matrix passes the consistency check, determining the maximum characteristic root of the importance scoring matrix; carrying out normalization processing on the feature vector corresponding to the maximum feature root to obtain an index weight matrix corresponding to the level of target index set; the index weight matrix is used for representing a weight value corresponding to each target index in the level target index set, and the sum of the weight values corresponding to each target index in the level target index set is 1.
In one embodiment, determining the sharing effect evaluation result corresponding to the data sharing job according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index includes: for each target index, determining a weight value corresponding to the target index under each level of the target index set based on the index weight matrix corresponding to each level of the target index set; determining the product of each weight value corresponding to the target index as a target weight value corresponding to the target index set; and carrying out weighted summation on the target sharing effect scores corresponding to each target index based on the target weight values corresponding to each target index to obtain a sharing effect evaluation result corresponding to the data sharing work.
In one embodiment, the data preprocessing is performed on the sharing effect scoring data, and determining a target sharing effect score corresponding to each target index includes: and for each target index, determining the average value of each initial sharing effect score corresponding to the target index as a target sharing effect score corresponding to the target index.
In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating a data sharing effect in a transportation planning industry, including: the data acquisition module is used for acquiring sharing effect scoring data corresponding to the data sharing work to be evaluated based on a pre-established target index system; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level; the preprocessing module is used for preprocessing the data of the sharing effect scoring data and determining a target sharing effect score corresponding to each target index; and the effect evaluation module is used for determining a sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index.
In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory storing computer-executable instructions executable by the processor to implement the method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspects.
According to the method and the device for evaluating the data sharing effect in the traffic planning industry, firstly, sharing effect scoring data corresponding to data sharing work to be evaluated is obtained based on a pre-established target index system, wherein the target index system comprises a multi-level target index set, the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level, then data preprocessing is carried out on the sharing effect scoring data, the target sharing effect score corresponding to each target index is determined, and finally, the sharing effect evaluation result corresponding to the data sharing work is determined according to an index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index. The method comprises the steps of evaluating the data sharing work to be evaluated based on the target index system, obtaining corresponding sharing effect scoring data, preprocessing the data to obtain target sharing effect scores corresponding to each target index, further combining an index weight matrix corresponding to the target index system and the target sharing effect scores corresponding to each target index to determine a sharing effect evaluation result corresponding to the data sharing work.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for evaluating a data sharing effect in a transportation planning industry according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for evaluating data sharing effects in another transportation planning industry according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an evaluation device for data sharing effect in the transportation planning industry according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The digital transformation of the traffic industry gradually changes from the external field construction to the data application-based stage, and the introduction and the deep penetration of technologies such as cloud computing, big data, artificial intelligence and the like help the rapid development of the transformation. And the requirement of 'social management modernization' promotes data sharing, multiplexing and cross-department and cross-field trend. However, currently there are few scientific data sharing mechanisms or systems for an industry (such as the field of comprehensive transportation planning), and thus there is insufficient research or application related to how to evaluate the effect of data sharing in the industry. Based on the method and the device, the data sharing effect evaluation method and the device in the transportation planning industry can evaluate the data sharing effect truly, accurately and comprehensively.
For the convenience of understanding the present embodiment, first, a detailed description will be given of a method for evaluating a data sharing effect in a transportation planning industry disclosed in the present embodiment, referring to a schematic flow chart of evaluating a data sharing effect in a transportation planning industry shown in fig. 1, the method mainly includes the following steps S102 to S106:
step S102, sharing effect scoring data corresponding to the data sharing work to be evaluated is obtained based on a pre-established target index system. The target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of the designated level, namely a final target index set. In one embodiment, a target index system is provided for at least one scoring participant in the form of paper or electronic document, and the scoring participant scores the sharing effect corresponding to each target index in the final target index set, so that at least one initial sharing effect score corresponding to each target index can be obtained. The target index system is assumed to comprise three levels of target index sets, wherein the first level of target index sets comprise a plurality of first levels of targets, the second level of target index sets are sub-index sets of the first level of target index sets and comprise a plurality of second levels of targets, and the third level of target index sets are sub-index sets of the second level of target index sets and comprise a plurality of third levels of targets. In specific implementation, scoring participants only need to score each three-level index to obtain at least one initial sharing effect score corresponding to each three-level index.
Step S104, data preprocessing is carried out on the sharing effect scoring data, and the target sharing effect score corresponding to each target index is determined. Wherein the data preprocessing includes averaging processing. In one embodiment, for each target indicator, the average of each initial sharing effect score corresponding to the target indicator may be used as the target sharing effect score corresponding to the target indicator.
Step S106, according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index, determining the sharing effect evaluation result corresponding to the data sharing work. The index weight matrix comprises an index weight matrix corresponding to each level of target index set, such as an index weight matrix corresponding to a first level of target index set, an index weight matrix corresponding to a second level of target index set and an index weight matrix corresponding to a third level of target index set, wherein elements in the index weight matrix represent weight values of each target index in the level of target index set. In one embodiment, taking a target index system including three levels of target index sets as an example, the weight value of each target index under the three-level target index set, the weight value under the two-level target index set and the weight value under the one-level target index set need to be determined respectively, and the product of each weight value is determined as the total weight value of the target index, so that the target sharing effect scores corresponding to each target index are weighted and summed according to the total weight value of each target index, and the sharing effect evaluation result corresponding to the data sharing work can be obtained.
According to the evaluation method for the data sharing effect in the traffic planning industry, the data sharing effect to be evaluated is evaluated based on the target index system, corresponding sharing effect scoring data is obtained, data preprocessing is carried out on the data sharing effect scoring data to obtain target sharing effect scores corresponding to all target indexes, and then sharing effect evaluation results corresponding to the data sharing operation are determined by combining the index weight matrix corresponding to the target index system and the target sharing effect scores corresponding to all target indexes.
For easy understanding, the embodiment of the invention firstly explains the configuration process of the target index system and the index weight matrix corresponding to the target index system.
The embodiment of the invention provides an implementation mode for constructing a target index system, which is shown in the following steps 1 to 3:
step 1, acquiring a pre-established initial index system and an index scoring result corresponding to the initial index system. The initial index system comprises a multi-level initial index set, the index scoring result comprises at least one single index importance score corresponding to each initial index in the initial index set of the designated level, the initial index set of the designated level is a final initial index set in the initial index system, and the single index importance score is used for reflecting the importance degree of a single initial index.
Taking the establishment of a data sharing effect evaluation system in the comprehensive transportation planning field as an example, the system comprises a target layer, a first-level index (also called a criterion layer), a second-level index (also called a sub-criterion layer) and a third-level index (also called an index layer) from top to bottom. The method is characterized in that the existing assessment indexes about data sharing of the government are combed, the data sharing evaluation index system of relevant departments and scientific research institutions in each place is known by combining a literature reference method and a text analysis method, and the evaluation index system is primarily determined according to the systematic, scientific and operable combination principle and the quantitative and qualitative combination principle of the index system construction.
The embodiment of the invention uses data resources, service effects, data application and guarantee mechanisms as comprehensive transportation planning data to share a first-level evaluation index by referring to DCMM (data management capability maturity assessment model), scientific data sharing engineering of a technical department and the like proposed by GB/36073-2018, designs 15 second-level indexes belonging to the category of the first-level indexes and 34 third-level indexes belonging to the category of the second-level indexes on the basis of the self-properties of comprehensive transportation planning services according to an evaluation index system construction principle, and finally carries out hierarchical division on the indexes by using a hierarchical structure of a hierarchical analysis method. The target layer is a target to be evaluated, and in the embodiment of the invention, the data sharing effect evaluation in the comprehensive transportation planning field is performed; the criterion layer is the core of the whole structure, upwards receives the target of the corresponding overall evaluation, downwards depends on the indexes of the subsequent level, and is the first-level index; the sub criterion layer is a specific decomposition of the upper layer index, and can realize the detailed evaluation of the target layer as a second-level index; and further decomposing the secondary index into a tertiary index on the basis of the sub-criterion layer. See table 1 below for specific index cases:
TABLE 1
And 2, judging whether the initial index meets the preset index condition or not based on the index grading result for each initial index. The initial index may have a certain subjective bias, the initial index system is further improved by a certain method based on the initial index, the expert is invited to carry out anonymous assessment on the formulated index system by adopting a Likett scale method, and the preliminary index is screened by a variation coefficient method, a membership degree method and a weighted average method, so that the scientificity of the preliminary index is ensured. In one embodiment, it may be determined whether the initial indicator meets a preset indicator condition according to the following steps 2.1 to 2.2, specifically:
and 2.1, respectively determining an importance weighted average value, a variation coefficient and membership corresponding to the initial index based on the index scoring result. For ease of understanding, embodiments of the present invention provide implementations for calculating importance weighted averages, coefficients of variation, and membership, respectively, see (1) to (3) below:
(1) And determining an importance weighted average corresponding to the initial index based on each single index importance score corresponding to the initial index and the first number of single index importance scores. The weighted importance average value can reflect the concentration degree of expert scores, and is positively related to the relative importance, and is also called as an importance average value, and the calculation formula is as follows:
Wherein Ci in the formula represents an importance weighted average value of the initial index i; c (C) ij Scoring the single index importance degree of the initial index i for the expert j; m is the number of all experts (i.e., the first number) participating in the scoring.
(2) And determining the scoring standard deviation corresponding to the initial index based on the importance score of each single index corresponding to the initial index, and determining the ratio of the scoring standard deviation to the importance weighted average corresponding to the initial index as the variation coefficient corresponding to the initial index. Wherein, the smaller the variation coefficient is, the higher the coordination degree of the expert is, the corresponding index item should be reserved; conversely, the higher the coefficient of variation, the lower the degree of coordination of the expert, the corresponding index term should be eliminated, and the coefficient of variation is calculated as follows:
wherein V is i The variation coefficient of the initial index i, C i Weighted average of importance of initial index i, S i The standard deviation of the score for the initial index i.
(3) Determining the total number of single index importance scores corresponding to the initial index and the second number of single index importance scores larger than a preset scoring threshold, and determining the ratio of the second number to the total number as the membership corresponding to the initial index. Illustratively, the score assignment for each initial indicator may be set to 1-5 and the preset score threshold may be set to 3. The membership degree can reflect the degree of the initial index belonging to the initial index set, and the membership degree calculation formula is as follows:
Wherein k represents the kth initial index, Y K Representing the total number of people involved in the survey (i.e., the total number), W K Indicating a total number of people (i.e., a second number) selected for 3 or more points in the score, R k Representing the membership of the kth initial indicator.
And 2.2, if the weighted average of the importance is larger than a preset importance threshold, the variation coefficient is smaller than a preset variation coefficient threshold, and the membership is larger than a preset membership threshold, determining that the initial index meets a preset index condition. For example, the weighted average of importance is greater than 3, and the initial index can be reserved, that is, the importance threshold is set to 3; alternatively, the coefficient of variation should not be greater than 15%, so the coefficient of variation threshold is set to 15%; the membership degree screening index critical value is set to 0.6, which means that more than 60% of expert students consider that the index importance degree is 'general', the initial index can be reserved, and if the membership degree is lower than 0.6, the initial index can be eliminated, namely, the membership degree threshold value is set to 60%. Based on the above, if the weighted average of importance of the initial index is greater than 3, the variation coefficient is less than 15%, and the membership is greater than 60%, determining that the initial index meets the preset index condition, and retaining the initial index; otherwise, if the weighted average of the importance of the initial index is smaller than 3, or the variation coefficient is larger than 15%, or the membership is smaller than 60%, the initial index is determined to not meet the preset index condition, and the initial index is removed.
And 3, if so, determining the initial index as a target index, and constructing a target index system based on each target index. In particular implementations, each target indicator will constitute a target indicator system.
Illustratively, according to the steps 1 to 3, it may be determined that the 4 indexes in the questionnaire have a variation coefficient of 15% or more and that the 30 three-level indexes have a variation coefficient of less than 15%. The result shows that most indexes have compact score variation, and each expert has higher coordination degree and good coordination degree, so that the opinion of the expert on the indexes tends to be consistent.
For example, in order to construct a data sharing effect evaluation index system in the comprehensive transportation planning field, an expert investigation scheme is designed, the purpose and the task of the investigation and the description of other important matters are described in detail, the investigation scheme adopts a Likert 5-level scale model, and each expert scores the importance degree of 17 secondary indexes and 34 tertiary indexes according to the understanding of the expert field of the comprehensive transportation planning, the score is assigned to be 1-5, and the expert is more authorized to the indexes, and the score of the indexes is higher. The number of the specialists participating in the questionnaire survey is 10, and the requirements that the number of the specialists reaches more than 5 according to the specialist scoring method are met. The expert range participating in the survey covers advanced engineers, assistant professors, students and authoritative degree in the comprehensive transportation planning field. In addition, 10 parts of expert questionnaires are collected in total and are all effective questionnaires, so that the effective rate of the recovered questionnaires is 100%, and the statistical requirements are met, as shown in the following table 2:
TABLE 2
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In the 34 secondary indexes of the preliminary design, the membership degree of 30 indexes is higher than 0.6, the importance average value is higher than 3, and the variation coefficients of the 30 indexes are lower than the variation coefficient threshold value, so that the final evaluation index is determined. Therefore, the total of 4 secondary evaluation indexes to be eliminated are respectively the conditions of high-value data quantity, data repeatability, data interpretation degree, output patents, papers and the like, and finally the data sharing effect evaluation indexes (namely, target index system) in the comprehensive transportation planning field are determined to be composed of 4 primary indexes, 15 secondary indexes and 30 tertiary indexes, as shown in the following table 3:
TABLE 3 Table 3
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Further, the index scoring result further comprises a multi-index importance score between any two initial indexes in each level of initial index set. In one embodiment, the relative importance of the lower layer elements to the upper layer elements is compared by the target layer, the criterion layer, the sub-criterion layer and the index layer to establish a judgment matrix (i.e. an index weight matrix); and calculating the single-order numerical value of each layer of the judgment matrix, and obtaining the relative weight of the index layer to the criterion layer and the relative weight of the criterion layer to the target layer, namely obtaining the weight vector of the judgment matrix.
For easy understanding, the embodiment of the present invention provides an implementation manner of constructing an index weight matrix, and specifically, reference may be made to the following steps a to d:
and a, constructing an importance scoring matrix corresponding to each level of target index set. The importance degree scoring matrix comprises a plurality of target indexes, wherein each target index is contained in the target index set, and each target index is characterized by a plurality of importance degree scoring elements. In one embodiment, after the hierarchical structure system is established, the specific gravity of each factor and each subordinate index are compared, and the judgment matrix table is finally obtained by combining expert scoring. And comparing indexes or factors of each layer in pairs according to the importance level scales of 1-9, and quantifying the importance level by using numerical values so as to construct a mathematical judgment matrix. The 1 to 9 scale is shown in table 4 below:
TABLE 4 Table 4
| Proportional scale | Meaning of |
| 1 | The two indexes have the same importance compared with each other |
| 3 | The former is slightly more important than the latter than the two indexes |
| 5 | The former is obviously important than the latter in comparison with the two indexes |
| 7 | The former is more important than the latter in comparison with the two indexes |
| 9 | The former is extremely important than the latter in comparison with the two indexes |
Illustratively, firstly scoring multi-index importance scores between any two three-level indexes in the three-level target index set, for example, scoring multi-index importance scores (such as score of 6) between traffic industry data coverage and business related economy society, and traversing each three-level index in a similar way to obtain multi-index importance scores between any two three-level indexes in the three-level target index set; further scoring a multi-index importance score between any two secondary indexes in the secondary target index set; and scoring the multi-index importance scores between any two primary indexes in the primary target index set.
And b, carrying out consistency check on the importance scoring matrix. Considering that logic errors may occur when constructing the judgment matrix, for example, the three-level index a is more important than the three-level index B, which is more important than the three-level index C, but the three-level index C is more important than the three-level index a. It is therefore necessary to use a consistency check to analyze if a problem arises, using a CR value (i.e., a critical ratio) that is less than 0.1 to indicate that the consistency check is passed, and vice versa to indicate that the consistency check is not passed.
If the data does not pass the consistency test, whether logic problems exist or not is checked, and the index weight matrix is re-input for analysis. The calculation formula for the CR value is as follows:
wherein λmax is the maximum characteristic root of the index weight matrix, m is the square matrix order, and RI is the average consistency index.
And c, if the importance degree scoring matrix passes the consistency check, determining the maximum characteristic root of the importance degree scoring matrix.
And d, carrying out normalization processing on the feature vector corresponding to the maximum feature root to obtain an index weight matrix corresponding to the target index set of the level. The index weight matrix is used for representing a weight value corresponding to each target index in the level target index set, and the sum of the weight values corresponding to each target index in the level target index set is 1. In practical application, the feature vector represents the weight of each index of the level affecting the index of the previous level, so to obtain the weight, the maximum feature root λmax of the judgment matrix and the feature vector are calculated first, and consistency test is performed. If the feature vector W passes the inspection, the feature vector W corresponding to the maximum feature root λmax is normalized to be the calculated index weight.
In concrete implementation, determining an index weight matrix corresponding to the first-level target index set according to the steps a to d, wherein the matrix records a weight value corresponding to each first-level index; simultaneously determining an index weight matrix corresponding to the secondary target index set, wherein the matrix is recorded with weight values corresponding to each secondary index; and determining an index weight matrix corresponding to the three-level target index set, wherein the weight value corresponding to each three-level index is recorded in the matrix. Exemplary, the embodiment of the present invention provides a weight value corresponding to each three-level index as shown in table 5:
TABLE 5
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In one implementation, steps S102 to S106 may be performed on the basis of the foregoing examples. For ease of understanding, step S104 is explained first, and when the step of performing data preprocessing on the sharing effect score data to determine the target sharing effect score corresponding to each target indicator is performed, for each target indicator, an average value of each initial sharing effect score corresponding to the target indicator may be determined as the target sharing effect score corresponding to the target indicator.
In practical application, the data sharing participants are investigated by a questionnaire method. The study used for the design of the questionnaire consisted of a set of statements, each statement having five responses such as "very agreeable", "not necessarily", "disagreeable", "very disagreeable" and 5, 4, 3, 2, 1 respectively. The average score (i.e., the average of each initial share effect score) for each indicator can be obtained by summing and averaging the corresponding scores for each statement option of the participating investigators.
In the embodiment of the invention, 50 questionnaires are issued in the aspects of relevant researchers of a transportation department planning institute, data sharing management personnel, a hospital leader and the like, and 47 valid questionnaires are recovered. Firstly, analyzing the quality of questionnaire data, the confidence coefficient can be interpreted as how much proportion in the total variance is determined by the variance of the true score, namely the proportion of the variation caused by the true score in the total variation of the test. In the questionnaire credibility test, the credibility coefficient value is 0.905 and is larger than 0.9, so that the credibility quality of the research data is high. The data of the questionnaire can be directly calculated, and the normalization calculation is carried out on the index data source which is the system data, and the scores of the sharing effects of each target are shown in the following table 6:
TABLE 6
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For easy understanding, further explanation is given to step S106, when the step of determining the sharing effect evaluation result corresponding to the data sharing job according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index is performed, the following steps one to three may be referred to:
step one, for each target index, determining the target based on an index weight matrix corresponding to each level of target index set The index is corresponding to the weight value under each level of target index set. Taking the example that the target index is traffic industry data coverage as an example, firstly determining a three-level index weight value W corresponding to traffic industry data coverage from an index weight matrix corresponding to a three-level target index set r Determining a corresponding secondary index weight value W from an index weight matrix corresponding to a secondary target index set (namely, field coverage) to which the traffic industry data coverage belongs q Determining corresponding first-level index weight value W from index weight matrix corresponding to first-level target index set (namely data resource) of field coverage i 。
And step two, determining the product of each weight value corresponding to the target index as a target weight value corresponding to the target index set. In one embodiment, a combined weight vector is calculated, i.e. the weight value of the target index of each layer relative to the total target importance, and the weight value of the three-level index relative to the total target is calculated as follows:
W i ＝W p ·W q ·W r
wherein W is i Weight of the secondary index relative to the total target, W p Is the first-level index weight, W q Is the weight of the secondary index under the primary index, W r Is the three-level index weight under the two-level index.
And thirdly, carrying out weighted summation on the target sharing effect scores corresponding to each target index based on the target weight value corresponding to each target index to obtain a sharing effect evaluation result corresponding to the data sharing work. In one embodiment, the target sharing effect scores are multiplied by corresponding target weight values and summed to obtain a total sharing effect evaluation result. Calculating the scoring condition of the data sharing effect in the comprehensive transportation planning field and the corresponding evaluation thereof, such as the following table 7:
TABLE 7
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The core of the data sharing work is to continuously promote the data sharing effect, so that the importance of the evaluation measure of the data sharing effect is self-evident. In order to promote the data sharing opening and development application in the comprehensive transportation planning field, the supervision management of the data sharing opening work is enhanced, the sharing work quality and the service level are improved, the standardization, the institutionalization and the scientization of the data sharing opening work in the field are realized, a sharing effect evaluation index system is required to be formulated in combination with the actual characteristics of the industry field, the data sharing effect is evaluated, and the data resource sharing in the field is further standardized and promoted. The sharing effect evaluation index system is comprehensively and scientifically constructed, the sharing effect level is accurately and timely monitored and reflected, the sharing effect evaluation index system is one of key tasks of data sharing work, and is also a key measure for enhancing the high-quality development of the data sharing work. The invention is based on the data co-construction sharing work of the transportation department planning institute, and the sharing effect score is obtained by combining subjective and objective aspects of determining the evaluation index, the index value, the weight and the like by methods of expert investigation, employee investigation and the like.
The embodiment of the invention also provides a specific implementation manner of the data sharing effect evaluation method in the transportation planning industry, referring to a flow chart of another data sharing effect evaluation method in the transportation planning industry shown in fig. 2, the method mainly comprises the following steps S202 to S220:
in step S202, the index system is initially selected (i.e., an initial index system is constructed).
In step S204, the index system is screened (i.e., the target index system is determined).
Step S206, constructing an index weight matrix.
In step S208, a weight vector is calculated (i.e., the maximum feature root and feature vector are determined).
Step S210, determining whether the consistency detection is passed. If yes, go to step S214; if not, step S212 is performed.
Step S212, the elements of the index weight matrix are adjusted, and step S206 is continued.
Step S214, calculating a target weight value.
Step S216, sharing effect scoring data is acquired.
Step S218, normalization of the sharing effect score data.
Step S220, calculating the sharing effect evaluation result.
In summary, the method for evaluating the data sharing effect in the traffic planning industry provided by the embodiment of the invention has at least the following characteristics:
(1) The method is characterized in that an index system is further improved through a certain method on the basis of the initial index, an expert is invited to carry out anonymous assessment on the formulated index system through a Liket scale method, and the initial index is screened through a coefficient of variation method, a membership method and a weighted average method, so that the scientificity of the initial index is ensured.
(2) Establishing a judgment matrix by comparing the relative importance of the lower layer elements to the upper layer elements by the target layer, the criterion layer and the index layer; and calculating the single-order numerical value of each layer of the judgment matrix, and obtaining the relative weight of the index layer aiming at the criterion layer and the relative weight of the criterion layer aiming at the target layer, namely obtaining the weight vector of the index weight matrix.
(3) And establishing a data sharing effect evaluation system in the field of comprehensive transportation planning by combining a literature reference method and a text analysis method, wherein the data sharing effect evaluation system comprises a target layer, a criterion layer formed by primary indexes, a second-level index and an index layer formed by tertiary indexes from top to bottom.
For the foregoing embodiment of the present invention to provide a method for evaluating a data sharing effect in a transportation planning industry, an embodiment of the present invention provides a device for evaluating a data sharing effect in a transportation planning industry, referring to a schematic structural diagram of a device for evaluating a data sharing effect in a transportation planning industry shown in fig. 3, the device mainly includes the following parts:
The data acquisition module 302 is configured to acquire sharing effect scoring data corresponding to a data sharing job to be evaluated based on a target index system established in advance; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level;
the preprocessing module 304 is configured to perform data preprocessing on the sharing effect scoring data, and determine a target sharing effect score corresponding to each target index;
the effect evaluation module 306 is configured to determine a sharing effect evaluation result corresponding to the data sharing job according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index.
The evaluation device for the data sharing effect in the traffic planning industry provided by the embodiment of the invention evaluates the data sharing work to be evaluated based on the target index system, acquires corresponding sharing effect scoring data, performs data preprocessing on the data scoring data to acquire the target sharing effect score corresponding to each target index, further combines the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index to determine the sharing effect evaluation result corresponding to the data sharing work.
In one embodiment, the apparatus further includes a system building module configured to: acquiring a pre-established initial index system and an index scoring result corresponding to the initial index system; the initial index system comprises a multi-level initial index set, the index scoring result comprises at least one single index importance score corresponding to each initial index in the initial index set of the designated level, and the initial index set of the designated level is a final initial index set in the initial index system; for each initial index, judging whether the initial index meets a preset index condition or not based on an index scoring result; if so, the initial index is determined as a target index, and a target index system is constructed based on each target index.
In one embodiment, the system building module is further configured to: based on the index scoring result, respectively determining an importance weighted average value, a variation coefficient and membership corresponding to the initial index; if the weighted average of the importance is larger than a preset importance threshold, the variation coefficient is smaller than a preset variation coefficient threshold, and the membership is larger than a preset membership threshold, the initial index is determined to meet a preset index condition.
In one embodiment, the system building module is further configured to: determining an importance weighted average corresponding to the initial index based on each single index importance score corresponding to the initial index and a first number of single index importance scores; and determining a scoring standard deviation corresponding to the initial index based on the importance score of each single index corresponding to the initial index, and determining the ratio of the scoring standard deviation to the importance weighted average value corresponding to the initial index as a variation coefficient corresponding to the initial index; and determining the total number of single-index importance scores corresponding to the initial indexes and the second number of single-index importance scores larger than a preset scoring threshold, and determining the ratio of the second number to the total number as the membership corresponding to the initial indexes.
In one embodiment, the index scoring result further includes a multi-index importance score between any two initial indexes in each level of initial index set; the device further comprises a weight configuration module for: for each level of target index set, constructing an importance scoring matrix corresponding to the level of target index set; wherein, the row and the column of the importance degree scoring matrix are each target index contained in the level target index set, and the elements of the importance degree scoring matrix are used for representing multi-index importance degree scoring between two target indexes; consistency verification is carried out on the importance scoring matrix; if the importance scoring matrix passes the consistency check, determining the maximum characteristic root of the importance scoring matrix; carrying out normalization processing on the feature vector corresponding to the maximum feature root to obtain an index weight matrix corresponding to the level target index set; the index weight matrix is used for representing a weight value corresponding to each target index in the level target index set, and the sum of the weight values corresponding to each target index in the level target index set is 1.
In one embodiment, the effect evaluation module 306 is further to: for each target index, determining a weight value corresponding to the target index under each level of target index set based on an index weight matrix corresponding to each level of target index set; determining the product of each weight value corresponding to the target index as a target weight value corresponding to the target index set; and carrying out weighted summation on the target sharing effect scores corresponding to each target index based on the target weight value corresponding to each target index to obtain a sharing effect evaluation result corresponding to the data sharing work.
In one embodiment, the preprocessing module 304 is further configured to: and for each target index, determining the average value of each initial sharing effect score corresponding to the target index as the target sharing effect score corresponding to the target index.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, the processor 40, the communication interface 43 and the memory 41 being connected by the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The memory 41 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and the at least one other network element is achieved via at least one communication interface 43 (which may be wired or wireless), which may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 42 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 41 is configured to store a program, and the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40 or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 40. The processor 40 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 41 and the processor 40 reads the information in the memory 41 and in combination with its hardware performs the steps of the method described above.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The method for evaluating the data sharing effect of the traffic planning industry is characterized by comprising the following steps of:
based on a pre-established target index system, obtaining sharing effect scoring data corresponding to the data sharing work to be evaluated; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level;
Performing data preprocessing on the sharing effect scoring data, and determining a target sharing effect score corresponding to each target index;
and determining a sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index.
2. The method for evaluating the data sharing effect of the transportation planning industry according to claim 1, wherein before obtaining the sharing effect scoring data corresponding to the data sharing job to be evaluated based on a pre-established target index system, the method further comprises:
acquiring a pre-established initial index system and an index scoring result corresponding to the initial index system; the initial index system comprises a multi-level initial index set, the index scoring result comprises at least one single index importance score corresponding to each initial index in the initial index set of the designated level, and the initial index set of the designated level is a final initial index set in the initial index system;
for each initial index, judging whether the initial index meets a preset index condition or not based on the index scoring result;
If so, determining the initial index as a target index, and constructing a target index system based on each target index.
3. The method for evaluating the data sharing effect of the transportation planning industry according to claim 2, wherein determining whether the initial index satisfies a preset index condition based on the index scoring result comprises:
based on the index scoring result, respectively determining an importance weighted average value, a variation coefficient and membership corresponding to the initial index;
and if the importance weighted average is greater than a preset importance threshold, the variation coefficient is smaller than a preset variation coefficient threshold, and the membership is greater than a preset membership threshold, determining that the initial index meets a preset index condition.
4. The method for evaluating the data sharing effect of the transportation planning industry according to claim 3, wherein the determining the weighted average of importance, the variation coefficient and the membership corresponding to the initial index based on the index scoring result comprises:
determining an importance weighted average corresponding to the initial index based on each single index importance score corresponding to the initial index and a first number of the single index importance scores;
And determining a scoring standard deviation corresponding to the initial index based on each single index importance score corresponding to the initial index, and determining the ratio of the scoring standard deviation to the importance weighted average corresponding to the initial index as a variation coefficient corresponding to the initial index;
and determining the total number of the single-index importance scores corresponding to the initial index and the second number of the single-index importance scores larger than a preset scoring threshold, and determining the ratio of the second number to the total number as the membership corresponding to the initial index.
5. The method for evaluating the data sharing effect of the transportation planning industry according to claim 2, wherein the index scoring result further comprises a multi-index importance score between any two initial indexes in each level of initial index set;
before determining the sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index, the method further comprises:
for each level of target index set, constructing an importance scoring matrix corresponding to the level of target index set; the system comprises a level target index set, an importance degree scoring matrix, a target index set and a target index set, wherein the line and the column of the importance degree scoring matrix are all target indexes contained in the level target index set, and the elements of the importance degree scoring matrix are used for representing multi-index importance degree scoring between the two target indexes;
Consistency verification is carried out on the importance scoring matrix;
if the importance scoring matrix passes the consistency check, determining the maximum characteristic root of the importance scoring matrix;
carrying out normalization processing on the feature vector corresponding to the maximum feature root to obtain an index weight matrix corresponding to the level of target index set; the index weight matrix is used for representing a weight value corresponding to each target index in the level target index set, and the sum of the weight values corresponding to each target index in the level target index set is 1.
6. The method for evaluating a data sharing effect in a transportation planning industry according to claim 5, wherein determining a sharing effect evaluation result corresponding to the data sharing job according to an index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index, comprises:
for each target index, determining a weight value corresponding to the target index under each level of the target index set based on the index weight matrix corresponding to each level of the target index set;
determining the product of each weight value corresponding to the target index as a target weight value corresponding to the target index set;
And carrying out weighted summation on the target sharing effect scores corresponding to each target index based on the target weight values corresponding to each target index to obtain a sharing effect evaluation result corresponding to the data sharing work.
7. The method for evaluating the data sharing effect of the transportation planning industry according to claim 1, wherein the step of performing data preprocessing on the sharing effect scoring data to determine a target sharing effect score corresponding to each target index comprises the steps of:
and for each target index, determining the average value of each initial sharing effect score corresponding to the target index as a target sharing effect score corresponding to the target index.
8. An evaluation device for data sharing effect in transportation planning industry, which is characterized by comprising:
the data acquisition module is used for acquiring sharing effect scoring data corresponding to the data sharing work to be evaluated based on a pre-established target index system; the target index system comprises a multi-level target index set, and the sharing effect scoring data comprises at least one initial sharing effect score corresponding to each target index in the target index set of a designated level;
The preprocessing module is used for preprocessing the data of the sharing effect scoring data and determining a target sharing effect score corresponding to each target index;
and the effect evaluation module is used for determining a sharing effect evaluation result corresponding to the data sharing work according to the index weight matrix corresponding to the target index system and the target sharing effect score corresponding to each target index.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1 to 7.
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