CN116433080A

CN116433080A - Data sharing scoring method and device for traffic transportation planning industry and electronic equipment

Info

Publication number: CN116433080A
Application number: CN202310271402.4A
Authority: CN
Inventors: 蹇峰; 石媛嫄; 顾明臣; 徐华军; 苗申; 黄叒; 黄兴华
Original assignee: Transport Planning And Research Institute Ministry Of Transport
Current assignee: Transport Planning And Research Institute Ministry Of Transport
Priority date: 2023-03-17
Filing date: 2023-03-17
Publication date: 2023-07-14
Anticipated expiration: 2043-03-17
Also published as: CN116433080B

Abstract

The invention provides a data sharing scoring method, a device and electronic equipment in the traffic planning industry, and relates to the technical field of comprehensive traffic planning. The quantitative evaluation mode based on five dimensions is suitable for industry characteristics and data co-construction sharing application scenes in the comprehensive traffic planning field, and realizes scientific and reasonable evaluation of data co-construction sharing in the comprehensive traffic planning field, so that data sharing open work in an organization can be effectively promoted, evaluation work efficiency is improved, and full release of data value of each department in the organization is realized.

Description

Data sharing scoring method and device for traffic transportation planning industry and electronic equipment

Technical Field

The invention relates to the technical field of comprehensive traffic planning, in particular to a data sharing scoring method, a data sharing scoring device and electronic equipment in the traffic planning industry.

Background

Objective checking of the data co-construction shared main body is an important content of continuous construction and benign development of data resources, directional guidance of the data resource construction is achieved through a perfect data sharing application mechanism and checking evaluation, construction of quality and quantity of the data resources is achieved, and sustainable development of the data resource construction and sharing application is promoted. The purpose of the data sharing work assessment is to reflect the work key points and guide the development direction, and improve the standardization, fairness, scientificity and rationality of sharing construction and operation management. And scientific decision basis is provided for continuous construction and sharing application of data through post-evaluation measures, so that benign sustainable development of data resource work in an organization is promoted.

At present, a scientific and reasonable evaluation system construction method is not available, and the shared open success in the comprehensive traffic planning field is evaluated.

Disclosure of Invention

The invention aims to provide a data sharing scoring method, a data sharing scoring device and electronic equipment in the traffic planning industry, so as to realize scientific and reasonable evaluation of data co-construction sharing in the comprehensive traffic planning field.

In a first aspect, an embodiment of the present invention provides a data sharing scoring method in a transportation planning industry, including:

Acquiring shared data information of a plurality of evaluation objects; the evaluation object comprises a department for implementing data co-construction sharing in the comprehensive traffic planning field;

according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object; the basic evaluation score comprises a data scale score, a data quality score, a data value score, a data application score and a sharing deduction score;

and determining the total data sharing score of each evaluation object according to the basic evaluation score of each evaluation object.

Further, the shared data information includes data scale values of various types of shared data; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

according to the data scale values of various types of data corresponding to the evaluation objects, calculating to obtain conversion coefficients of each type of data through a threshold method;

And calculating the data scale score of each evaluation object according to the data scale value of each type of data corresponding to each evaluation object and the conversion coefficient of each type of data.

Further, the various data comprise spatial data, business data, equipment data and statistical data; the calculation of the conversion coefficient of each type of data by a threshold method according to the data scale value of each type of data corresponding to each evaluation object comprises the following steps:

determining a comprehensive scale maximum value and a data scale maximum value of each type of data; the comprehensive scale maximum value is the maximum value in the data scale values of various data corresponding to each evaluation object; the maximum value of the data scale of each type of data is the maximum value of the data scale values of the type of data corresponding to each evaluation object;

and determining the ratio of the integrated scale maximum value to the data scale maximum value of each type of data as a conversion coefficient of each type of data.

Further, the shared data information comprises data quality values of the shared data under a plurality of preset quality indexes; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

And calculating the data quality score of each evaluation object according to the data quality values of the plurality of quality indexes corresponding to each evaluation object.

Further, the plurality of quality indicators includes normalization, integrity, accuracy, consistency, and timeliness; the data quality value under normalization is the ratio of the number of elements in the data set meeting the normalization requirement to the number of elements in the data set being evaluated; the data quality value under the completeness is the ratio of the number of the elements in the assigned data set to the number of the elements in the assigned data set in the expected space-time range; the data quality value under the accuracy is the ratio of the number of elements in the data set meeting the data accuracy requirement to the number of elements in the data set to be evaluated; the data quality value under consistency is the ratio of the number of elements in the data set consistent with the historical data to the number of elements in the data set to be evaluated; the data quality value under timeliness is the ratio of the number of elements in the data set that meet the validity requirement to the number of elements in the data set that are evaluated.

Further, the shared data information includes a first index value under a plurality of first value indexes corresponding to unconditional shared data of each data source and a second index value under a plurality of second value indexes corresponding to conditional shared data of each data source; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

According to first index values of unconditional shared data of various data sources corresponding to each evaluation object under each first value index, calculating a conversion coefficient and a range of each first value index through a threshold method; according to the first index value of the unconditional shared data of various data sources corresponding to each evaluation object under each first value index, and the conversion coefficient and the range of each first value index, calculating to obtain a first score of each data source of each evaluation object corresponding to the unconditional shared data;

according to the second index values of the conditional shared data of various data sources corresponding to each evaluation object under each second index, calculating the conversion coefficient and the range of each second index by a threshold method; calculating to obtain a second score of each data source of each evaluation object corresponding to the conditional shared data according to the second index value of the conditional shared data of each data source corresponding to each evaluation object under each second index value and the conversion coefficient and the range of each second index value;

the data value score of each of the evaluation objects is determined based on the first scores of the various data sources corresponding to the unconditionally shared data and the second scores of the various data sources corresponding to the conditionally shared data.

Further, the plurality of first price indicators includes an access number, a download number, a frequency of use, and a space-time range; the second value indexes comprise application quantity, acquisition quantity, use frequency and space-time range; the determining the data value score of each evaluation object according to the first score of each data source of the unconditional shared data and the second score of each data source of the conditional shared data, comprising:

determining a first maximum value corresponding to each data source of the unconditionally shared data and a second maximum value corresponding to each data source of the conditionally shared data; wherein the first maximum value is the maximum value in the first score of each data source of the unconditional shared data corresponding to each evaluation object, and the second maximum value is the maximum value in the second score of each data source of the conditional shared data corresponding to each evaluation object;

calculating to obtain an initial value score of each evaluation object according to a first score of each data source of the unconditional shared data and a second score of each data source of the conditional shared data, as well as a first adjustment coefficient corresponding to each first maximum value, a second maximum value and each data source of the unconditional shared data and a second adjustment coefficient corresponding to each data source of the conditional shared data;

And calculating the data value score of each evaluation object according to the initial value score of each evaluation object and the maximum value in the initial value scores of the respective evaluation objects.

Further, the shared data information comprises total duration of use of shared data by department personnel and the number of accounting persons of the department; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

according to the total use time length and the department accounting number corresponding to each evaluation object, calculating to obtain the average use time length of people corresponding to each evaluation object;

and determining the data application score of each evaluation object according to the average person use duration corresponding to each evaluation object and the maximum value in the average person use duration corresponding to each evaluation object.

In a second aspect, an embodiment of the present invention further provides a data sharing scoring device in a transportation planning industry, including:

the acquisition module is used for acquiring shared data information of a plurality of evaluation objects; the evaluation object comprises a department for implementing data co-construction sharing in the comprehensive traffic planning field;

The evaluation module is used for evaluating the data scale, the data quality, the data value, the data application and the sharing deduction in five dimensions according to the shared data information of each evaluation object to obtain a basic evaluation value of each evaluation object; the basic evaluation score comprises a data scale score, a data quality score, a data value score, a data application score and a sharing deduction score;

and the determining module is used for determining the total data sharing score of each evaluation object according to the basic evaluation score of each evaluation object.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, the processor implements the data sharing scoring method in the transportation planning industry of the first aspect.

According to the data sharing scoring method, device and electronic equipment for the traffic planning industry, a department for implementing data co-construction sharing in the comprehensive traffic planning field is taken as an evaluation object, shared data information of a plurality of evaluation objects is firstly obtained, then evaluation of data scale, data quality, data value, data application and sharing deduction five dimensions is carried out, a basic evaluation score of each evaluation object is obtained, and then the total data sharing score of each evaluation object is determined. The quantitative evaluation mode based on five dimensions is suitable for industry characteristics and data co-construction sharing application scenes in the comprehensive traffic planning field, and realizes scientific and reasonable evaluation of data co-construction sharing in the comprehensive traffic planning field, so that data sharing open work in an organization can be effectively promoted, evaluation work efficiency is improved, and full release of data value of each department in the organization is realized.

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 schematic flow chart of a data sharing scoring method in a transportation planning industry according to an embodiment of the present invention;

FIG. 2 is a flow chart of another method for scoring data sharing in the transportation planning industry according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a conversion coefficient assignment page according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a page of data deduction according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a system scoring page according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a data sharing scoring device in the transportation planning industry according to an embodiment of the present invention;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be clearly and completely described in connection 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.

In the data sharing open process, there are problems of small data resource scale, poor shared data quality, low shared data value, etc., and typical applications generated by using shared data inside an organization are small, so that good data scale economy is not formed. The existing assessment about open assessment of data sharing takes government departments as a main body, and some departments evaluate the open assessment through the dimensions of organization guarantee, data pushing quantity and quality, credit constraint, fault emergency treatment, operation of a department collaborative supervision platform, use conditions and the like; some departments evaluate through the dimensions of organization management, basic security, data sharing, data opening, data utilization and the like; some departments evaluate through dimensions such as shared environments, resource sharing, resource directory systems, and the like. Different departments construct an assessment system according to the actual data sharing condition of the departments, but the assessment system comprises partial qualitative assessment indexes, which are not beneficial to obtaining quantitative scores, and the related index system is not completely suitable for the industry characteristics and the data co-construction sharing application scene in the comprehensive traffic planning field.

Based on the above, the data sharing scoring method, device and electronic equipment in the traffic planning industry provided by the embodiment of the invention provide an objective computing method system with characteristics of industry aiming at the problem of open assessment of data sharing in the comprehensive traffic planning field, the method system provides shared data and uses the conditions of the industry to be brought into an evaluation system, and the score of different departments participating in data sharing can be obtained by setting a scientific and reasonable index system for computation, so that open work of data sharing in an organization can be effectively promoted, the evaluation work efficiency is improved, and the data value of each department in the organization is fully released.

For the convenience of understanding the present embodiment, first, a data sharing scoring method in the transportation planning industry disclosed in the present embodiment is described in detail.

The embodiment of the invention provides a data sharing scoring method in the transportation planning industry, which can be executed by electronic equipment with data processing capability. The method is based on the co-construction shared data of the transportation department planning institute, and through assessment index calculation, the departments are promoted to release and use the data to a greater extent, so that the data resources are flowed to generate greater value, and the co-construction shared of the data across departments, levels and fields is promoted to be truly realized. Referring to a flow chart of a data sharing scoring method in the transportation planning industry shown in fig. 1, the method mainly includes the following steps S102 to S106:

Step S102, obtaining shared data information of a plurality of evaluation objects; the evaluation object comprises a department which implements data co-construction sharing in the comprehensive traffic planning field.

In the embodiment of the invention, the data co-construction sharing administration department in the comprehensive traffic planning field is taken as an evaluation subject, and in the open working range of the organization data co-construction sharing, the department for implementing the data co-construction sharing is taken as an evaluation object, namely the evaluation object is the data sharing subject, and the evaluation subject performs data co-construction sharing assessment on each evaluation object. The shared data information of each evaluation object may be information obtained by performing data statistics based on the data shared by the evaluation object.

Step S104, according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object; the basic evaluation score comprises a data scale score, a data quality score, a data value score, a data application score and a sharing deduction score.

In this embodiment, the evaluation of the data size, the data quality, the data value, the data application and the sharing deduction in five dimensions may be performed in the following manner:

(1) Data Scale assessment

The data scale score can be obtained by weighting, summing and normalizing the data scale of various types of data shared by different data sharing subjects (i.e., evaluation objects).

In some possible embodiments, the shared data information includes a data size value of each type of data shared; the data scale evaluation can be performed by the following procedure: according to the data scale values of various data corresponding to each evaluation object, calculating to obtain the conversion coefficient of each type of data by a threshold method; and calculating to obtain the data scale score of each evaluation object according to the data scale value of each type of data corresponding to each evaluation object and the conversion coefficient of each type of data.

Wherein, the various data can comprise space data, service data, equipment data and statistical data; the manner of calculating the conversion coefficient of each type of data by the thresholding method can be as follows: determining a comprehensive scale maximum value and a data scale maximum value of each type of data; the comprehensive scale maximum value is the maximum value in the data scale values of various types of data corresponding to all evaluation objects, and the data scale maximum value of each type of data is the maximum value in the data scale values of the type of data corresponding to all evaluation objects; and determining the ratio of the maximum value of the comprehensive scale to the maximum value of the data scale of each type of data as a conversion coefficient of each type of data.

In one possible implementation, the data size value of each type of data may be obtained as follows: the data shared by each evaluation object is divided into space data (including raster data), service data (including real-time data such as intermodulation dynamic data, super data and the like), equipment data (including equipment original data such as AIS (Automatic Identification System, automatic ship identification system), GPS (Global Positioning System ), mobile phone signaling and the like) and statistical data (including vector data and result index data such as traffic annual statistics, highway maintenance annual report, various statistical annual notices and the like), and data scale values (in KB) of various data are classified and counted, so that the data scale after actual sharing and warehousing is calculated. Wherein, the raster data is a kind of geographic space data, which is used for carrying out geographic coding on the map and filling in the information related to the surface features; intermodulation dynamic data refers to traffic survey dynamic data.

(2) Data quality assessment

In some possible embodiments, the shared data information includes a data quality value of the shared data under a plurality of preset quality indexes; the data quality assessment can be performed by the following procedure: and calculating the data quality score of each evaluation object according to the data quality values of the plurality of quality indexes corresponding to each evaluation object. Wherein, the data quality score of each evaluation object is positively correlated with the product of the data quality values under a plurality of quality indexes corresponding to the evaluation object.

The data quality scores can be comprehensively considered on normalization, integrity, accuracy, consistency and timeliness of data shared by different data sharing subjects. Wherein, standardability refers to the degree to which data accords with data standards, data models, business rules, metadata or authoritative reference data; integrity refers to the degree to which data elements are assigned values as required by the data rules; accuracy refers to the degree to which data accurately represents the true value of the real entity (real object) to which it is described; consistency refers to the degree to which data is not inconsistent with other historical data; timeliness refers to the degree of correctness of the data in terms of time.

Based on this, the plurality of quality indicators includes normalization, integrity, accuracy, consistency, and timeliness; the data quality value under normalization is the ratio of the number of elements in the data set meeting the normalization requirement to the number of elements in the data set being evaluated; the data quality value under the completeness is the ratio of the number of the elements in the assigned data set to the number of the elements in the assigned data set in the expected space-time range; the data quality value under the accuracy is the ratio of the number of elements in the data set meeting the data accuracy requirement to the number of elements in the data set to be evaluated; the data quality value under consistency is the ratio of the number of elements in the data set consistent with the historical data to the number of elements in the data set to be evaluated; the data quality value under timeliness is the ratio of the number of elements in the data set that meet the validity requirement to the number of elements in the data set that are evaluated. Wherein, the expected space-time range refers to an expected time range or a space range, for example, if the whole country is mentioned, 31 provincial data should be included, i.e. the number of elements is 31; if reference is made to half a year data, 6 months of data should be included, i.e. the number of elements is 6.

(3) Data value assessment

The sharing data can be divided into unconditional sharing data and conditional sharing data, and data users of the unconditional sharing data can download and use the unconditional sharing data by themselves, and the conditional sharing data is authorized to use after application. The unconditionally shared data and the conditionally shared data may be divided into various data according to data sources, for example: dividing unconditional shared data into public release data corresponding to a public source, intra-organization information exchange data and formally published project acquisition data, intra-industry information exchange data and project acquisition data corresponding to an intra-industry source, and off-industry information exchange data and project acquisition data corresponding to an off-industry source; the conditional shared data is divided into intra-organization information exchange data corresponding to an intra-organization source, intra-industry information exchange data and project collection data corresponding to an intra-industry source, and off-industry information exchange data and project collection data corresponding to an off-industry source.

Based on this, in some possible embodiments, the shared data information includes a first index value under a plurality of first value indexes corresponding to unconditional shared data of each data source and a second index value under a plurality of second value indexes corresponding to conditional shared data of each data source; the data value evaluation can be performed by the following procedure: according to the first index value of the unconditional shared data of various data sources corresponding to each evaluation object under each first value index, calculating to obtain a conversion coefficient and a margin of each first value index through a threshold method, and according to the first index value of the unconditional shared data of various data sources corresponding to each evaluation object under each first value index, and the conversion coefficient and the margin of each first value index, calculating to obtain a first score of each data source of each evaluation object corresponding to the unconditional shared data; according to the second index value of the conditional shared data of various data sources corresponding to each evaluation object under each second index value, calculating to obtain the conversion coefficient and the range of each second index value through a threshold method, and according to the second index value of the conditional shared data of various data sources corresponding to each evaluation object under each second index value, and the conversion coefficient and the range of each second index value, calculating to obtain the second conversion value of each data source of each evaluation object corresponding to the conditional shared data; the data value score for each evaluation object is determined based on the first scores of the various data sources for the unconditionally shared data and the second scores of the various data sources for the conditionally shared data for the respective evaluation objects.

Optionally, the data value score may be comprehensively measured from the number of accesses, the number of downloads, the frequency of use, the space-time range, and the like, and specifically, the unconditionally shared data may be measured from the number of accesses, the number of downloads, the frequency of use, the space-time range, and other dimensions; the conditional shared data can measure the data value from the dimensions of the application quantity, the acquisition quantity, the use frequency, the space-time range and the like. Based on this, the plurality of first price indicators may include the number of accesses, the number of downloads, the frequency of use, and the spatiotemporal range; the plurality of second value indicators may include an application number, an acquisition number, a frequency of use, and a space-time range.

The data value score of each evaluation object may be related to a first score of each data source of the evaluation object corresponding to the unconditionally shared data and a second score of each data source of the conditionally shared data, e.g., the data value score of each evaluation object may be obtained by weighted summation of each first score and each second score of each evaluation object. In one possible implementation, in order to increase the rationality of the data value evaluation, the influence of the maxima is also considered when weighting and summing the respective first scores and the respective second scores for each evaluation object, based on which, according to the first scores and the second scores of the respective evaluation objects for the respective data sources of the unconditionally shared data, the determination of the data value score for each evaluation object can be achieved by: first, determining a first maximum value corresponding to each data source of unconditionally shared data and a second maximum value corresponding to each data source of conditionally shared data; wherein the first maximum value is the maximum value in the first score of each data source of the unconditional shared data corresponding to each evaluation object, and the second maximum value is the maximum value in the second score of each data source of the conditional shared data corresponding to each evaluation object; then, according to the first scores of the various data sources of the unconditional shared data and the second scores of the various data sources of the conditional shared data corresponding to each evaluation object, and the first adjustment coefficients of the various data sources of the unconditional shared data and the second adjustment coefficients of the various data sources of the conditional shared data corresponding to each first maximum value, each second maximum value and the first adjustment coefficients of the various data sources of the unconditional shared data (namely, based on the first scores of the various data sources of the unconditional shared data and the second adjustment coefficients of the various data sources of the conditional shared data corresponding to each first maximum value, each second maximum value and the first scores of the various data sources of the unconditional shared data, the second scores of the various data sources of the unconditional shared data corresponding to each evaluation object are weighted and summed to obtain an initial value score of each evaluation object; and finally, calculating the data value score of each evaluation object according to the initial value score of each evaluation object and the maximum value in the initial value scores of the evaluation objects.

The first maximum value corresponds to the data source of the unconditional shared data one by one, and the second maximum value corresponds to the data source of the conditional shared data one by one, for example, the first maximum value corresponding to the public source of the unconditional shared data is the maximum value in the first scores of the respective evaluation objects corresponding to the public sources of the unconditional shared data. The first adjustment coefficients corresponding to various data sources of the unconditionally shared data and the second adjustment coefficients corresponding to various data sources of the conditionally shared data are preset, for example, the first adjustment coefficients corresponding to the public source, the intra-industry source and the off-industry source of the unconditionally shared data are respectively 1, 3 and 10, and the second adjustment coefficients corresponding to the intra-organization source, the intra-industry source and the off-industry source of the conditionally shared data are respectively 3, 5 and 10. The initial value score of each evaluation object may be normalized based on a maximum value of the initial value scores of the respective evaluation objects to obtain a data value score for each evaluation object.

(4) Data application evaluation

The data application evaluation focuses on checking the use condition of shared data in all departments (namely evaluation objects) of the data co-construction and sharing, and is mainly based on the use records of platform data by all department personnel reflected by the log records of the data sharing platform.

In some possible embodiments, the data application score may examine the usage time of the people-average data sharing platform of each participating department (i.e., the evaluation object), based on which the shared data information includes the total usage time of the shared data by the department personnel and the number of accounting persons by the department; the data application evaluation may be performed by the following procedure: according to the total using time length and the department accounting number corresponding to each evaluation object, calculating to obtain the average using time length corresponding to each evaluation object; and determining the data application score of each evaluation object according to the average person use duration corresponding to each evaluation object and the maximum value in the average person use duration corresponding to each evaluation object.

The average person use time length corresponding to each evaluation object can be normalized based on the maximum value in the average person use time length corresponding to each evaluation object, so that the data application score of each evaluation object is obtained.

(5) Shared deduction assessment

The deduction value of the data sharing may be determined according to the specific situation.

Step S106, determining the total data sharing score of each evaluation object according to the basic evaluation score of each evaluation object.

The total data sharing score of each evaluation object may also be referred to as a composite score, where the composite score may be calculated by applying a score average to the data scale score, the data quality score, the data value score, and the data, and subtracting the shared deduction score, and the composite score may comprehensively reflect the data sharing condition of each affiliated institution (i.e., the evaluation object).

The data sharing scoring method in the traffic planning industry provided by the embodiment of the invention takes a department for implementing data co-construction sharing in the comprehensive traffic planning field as an evaluation object, firstly acquires shared data information of a plurality of evaluation objects, then evaluates the data scale, the data quality, the data value, the data application and the sharing deduction in five dimensions to obtain a basic evaluation score of each evaluation object, and further determines the total data sharing score of each evaluation object. The quantitative evaluation mode based on five dimensions is suitable for industry characteristics and data co-construction sharing application scenes in the comprehensive traffic planning field, and realizes scientific and reasonable evaluation of data co-construction sharing in the comprehensive traffic planning field, so that data sharing open work in an organization can be effectively promoted, evaluation work efficiency is improved, and full release of data value of each department in the organization is realized.

For ease of understanding, the data sharing scoring method in the traffic planning industry is described below by taking the evaluation object as an example of 7 departments that implement data sharing in the comprehensive traffic planning field, with reference to fig. 2 to 4:

the embodiment of the invention mainly comprises the steps of basic data statistics, index weighting, score calculation and the like, wherein the score calculation step comprises the steps of calculating a data scale score, a data quality score, a data value score, a data application score, a sharing deduction score and a final data sharing total score.

1. Data Scale score (C) ₁ ) Calculation of

As shown in fig. 2, the division gate performs data size statistics of various data, and then weights the data by a threshold method to calculate the data size scores of the departments.

In this embodiment, the shared data is divided into spatial data, service data, device data and statistical data, and the specific calculation formula of the data scale score is as follows:

V ₁ ＝a ₁ N+a ₂ P+a ₃ D+a ₄ K

C ₁ ＝V ₁ /V _1max ×100

wherein N, P, D, K is the data scale value of the spatial data, the business data, the equipment data and the statistical data respectively; a, a ₁ 、a ₂ 、a ₃ 、a ₄ The conversion coefficients of various data are respectively used for the order-of-magnitude balance adjustment of different types of data scales; v (V) _1max V corresponding to each department ₁ Is the maximum value of (a).

Comparing the data scale maximum value of all data with the data scale maximum value of various types of data by a threshold method, and determining the conversion coefficient of each type of data, wherein the specific formula is as follows:

a ₁ ＝max{N，P，D，K}/N _max

a ₂ ＝max{N，P，D，K}/P _max

a ₃ ＝max{N，P，D，K}/D _max

a ₃ ＝max{N，P，D，K}/K _max

wherein, max { N,p, D, K is the maximum value in the data scale values of various data corresponding to each department, N _max For the maximum value of the data scale values of the space data corresponding to each department, P _max D, for the maximum value in the data scale values of the business data corresponding to each department _max For the maximum value, K, in the data scale values of the corresponding device data of each department _max And (5) the maximum value in the data scale values of the corresponding statistical data of each department.

Illustratively, the data size values of the various types of data for each department are shown in table 1 below:

TABLE 1

Department(s)	N	P	D	K
					Department
1	107666.000	0.000	0.000	0.000
					Department 2	8303124.480	2100782.400	0.000	352591.200
Department 3	7531.760	6150.400	15890571591.680	35330880.200
					Department 4	3029276.800	1789132.800	5296231546.880	1429.000
Department 5	1102848.000	114742.560	1872409.600	104322.200
					Department 6	562176.000	525105152.000	2554331136.000	0.000
Department 7	4647936.000	253068.800	71076311.040	0.000

The calculation of the conversion coefficient by thresholding is as follows:

a ₁ ＝15890571591.680/8303124.480＝1913.806；

a ₂ ＝15890571591.680/525105152.000＝30.262；

a ₃ ＝15890571591.680/15890571591.680＝1；

a ₄ ＝15890571591.680/35330880.200＝449.764。

the data scale score calculation results for each department are shown in table 2 below:

TABLE 2

Department(s)	C ₁	V ₁
			Department 1	0.65	206051864.586
Department 2	50.68	16112727790.581
			Department 3	100.00	31795743634.307
Department 4	35.06	11148465347.447
			Department 5	6.80	2162902528.105
Department 6	61.39	19520798674.639
			Department 7	28.22	8973983605.795

2. Data quality score (C) ₂ ) Calculation of

The embodiment comprehensively considers the data quality according to the normalization, the integrity, the accuracy, the consistency and the timeliness of the data. As shown in fig. 2, statistics such as data normalization and integrity are performed by the division gate, and then the data quality scores of the departments are calculated.

The specific calculation formula is as follows:

C ₂ ＝A/A _str ×B/B _str ×C/C _str ×D/D _str ×E/E _str ×100

wherein:

a is the number of elements in the data set meeting the normative requirement, and the data set meeting the normative requirement is a data set formed by data meeting the normative requirement in the data shared by the departments, and is similar to the following steps;

A _str for the number of elements in the data set to be evaluated, the data set to be evaluated is a data set formed by the data shared by the departments;

b is the number of elements in the assigned data set, and the assigned data set is a data set formed by assigned data in the data shared by the departments;

B _str Providing the number of the elements in the data set assigned in the expected space-time range for the department or the personnel;

c is the number of elements in the data set meeting the data correctness requirement (without error data, repeated data and messy code data);

C _str for elements in the data set being evaluatedIs the number of (3);

d is the number of elements in the dataset consistent with the historical data (including the data source, the data format, the data elements and the like;

D _str the number of elements in the data set being evaluated;

e is the number of elements in the dataset that meet the validity requirements (no time period, time point, timing error);

E _str is the number of elements in the dataset being evaluated.

Exemplary, the data quality values and data quality score calculation results for 5 quality indicators for each division are shown in table 3 below:

TABLE 3 Table 3

Department(s)

C ₂

A

Astr

B

Bstr

C

Cstr

D

Dstr

E

Estr

Department

1

100

10 ⁵

Department 2

100

10 ⁶

Department 3

90

9*10 ⁵

10 ⁶

Department 4

100

10 ⁶

Department 5

100

10 ⁶

Department 6

100

10 ⁶

Department 7

95

10 ⁶

9.5*10 ⁵

10 ⁶

3. Data value score (C) ₃ ) Calculation of

Dividing the shared data into two types of unconditional shared data and conditional shared data, wherein the unconditional shared data mainly carries out data value measurement from the dimensions of access quantity, download quantity, use frequency, space-time range and the like; the conditional shared data is mainly used for measuring the data value from the dimensions of the application quantity, the acquisition quantity, the use frequency, the space-time range and the like. The data sources of the unconditionally shared data include public sources, intra-industry sources, and off-industry sources, and the data sources of the conditionally shared data include intra-organization sources, intra-industry sources, and off-industry sources.

As shown in fig. 2, the division gate counts the access number, the download number and the like of the data under each data source, and then the data value score of each department is calculated by giving weight by a threshold value method.

The specific calculation formula is as follows:

V _3i ＝b ₁ R _i /(R _max -R _min) +b ₂ L _i /(L _max -L _min )+b ₃ F _i /(F _max -F _min )+b ₄ S _i /(S _max -S _min )

V′ _3j ＝b′ ₁ R′ _j /(R′ _max -R′ _min )+b′ ₂ L′ _j /(L′ _max -L′ _min )+b′ ₃ F′ _j /(F′ _max -F′ _min )+b′ ₄ S′ _j /(S′ _max -S′ _min )

C ₃ ＝C′ ₃ /C′ _3max ×100

wherein:

i is the number of the data source of the unconditional shared data, i=1, 2 and 3, and corresponds to the public source, the intra-industry source and the off-industry source of the unconditional shared data respectively; j is the number of the data source of the conditional shared data, j=1, 2, 3, and corresponds to the intra-organization source, the intra-industry source and the extra-industry source of the conditional shared data respectively;

R _i 、L _i 、F _i 、S _i the number of accesses (in terms of number of persons), the number of downloads (in terms of MB), the frequency of use (in terms of number of person hours), and the space-time range (in terms of year/province number) corresponding to the unconditionally shared data of the ith data source, respectively;

R′ _j 、L′ _j 、F′ _j 、S′ _j the number of applications (in terms of number of persons), the number of acquisitions (in terms of MB), the frequency of use (the number of hours of the person who uses the department to fold the data according to the data value) and the space-time range (in terms of year and province number) corresponding to the conditional shared data of the jth data source;

b ₁ 、b ₂ 、b ₃ 、b ₄ the method includes that the maximum value of all dimensions of the type of data (namely, the maximum value of all the unconditional shared data of all data sources corresponding to all evaluation objects under all the value indexes) is compared with the maximum value of the dimension of the type of data (namely, the maximum value of all the unconditional shared data of all the data sources corresponding to all the evaluation objects under the value indexes) through a threshold method, and the conversion coefficient is determined according to the following specific formula:

b ₁ ＝max{R，L，F，S}/R _max

b ₂ ＝max{R，L，F，S}/L _max

b ₃ ＝max{R，L，F，S}/F _max

b ₄ ＝max{R，L，F，S}/S _max

b′ ₁ 、b′ ₂ 、b′ ₃ 、b′ ₄ The method comprises the steps of comparing the maximum value of all dimensions of the data with the maximum value of the dimensions of the data through a threshold method to determine the conversion coefficient, wherein the conversion coefficient is respectively four value indexes of the application number, the acquisition number, the use frequency and the space-time range of the conditional shared data, and the specific formula is as follows:

b′ ₁ ＝max{R′，L′，F′，S′}/R′ _max

b′ ₂ ＝max{R′，L′，F′，S′}/L′ _max

b′ ₃ ＝max{R′，L′，F′，S′}/F′ _max

b′ ₄ ＝max{R′，L′，F′，S′}/S′ _max

γ _i the adjustment coefficient corresponding to the ith data source of unconditionally shared data. The method comprises the steps of taking 1 from public sources such as public release data, intra-organization information exchange data, formally published project acquisition data and the like; the in-industry source of the information exchange data and the project acquisition data in the industry is 3; an off-industry source fetch 10 comprising off-industry information exchange data and project acquisition data;

γ′ _j and the adjustment coefficient corresponding to the j-th data source of the conditional shared data. 3, taking an intra-organization source including intra-organization information exchange data; an intra-industry source fetch 5 comprising intra-industry information exchange data and project acquisition data; an off-industry source fetch 10 comprising off-industry information exchange data and project acquisition data;

max, min are the maximum and minimum values of each department of the index, e.g. R _max 、L _max 、F _max 、S _max The maximum value in the access quantity, the maximum value in the download quantity, the maximum value in the use frequency and the maximum value in the space-time range of unconditional shared data of various data sources corresponding to each department respectively; r is R _min 、L _min 、F _min 、S _min The method comprises the steps of respectively obtaining the minimum value in the access quantity, the minimum value in the download quantity, the minimum value in the use frequency and the minimum value in the space-time range of unconditional shared data of various data sources corresponding to each department; r's' _max 、L′ _max 、F′ _max 、S′ _max The method comprises the steps of obtaining the maximum value in the application quantity, the maximum value in the acquisition quantity, the maximum value in the use frequency and the maximum value in the space-time range of conditional shared data of various data sources corresponding to various departments; r's' _min 、L′ _min 、F′ _min 、S′ _min The minimum value in the application number, the minimum value in the acquisition number, the minimum value in the use frequency, the minimum value in the space-time range and the like of the conditional shared data of various data sources corresponding to each department are respectively used.

Illustratively, the value data of each type of data for each department are shown in tables 4 to 7 below:

TABLE 4 Table 4

TABLE 5

TABLE 6

TABLE 7

Calculating a conversion coefficient by a threshold method:

b1＝42942/42942＝1；

b2 The coefficient takes 1 when the denominator is 0, =42942/0; b3 42942/607.65 = 70.67;

b3＝42942/527＝81.48；

b′ ₁ ＝b′ ₂ ＝b′ ₃ ＝b′ ₄ when the denominator is 0, the coefficient takes 1.

The conversion factor assignment page is shown in fig. 3.

The data value score calculation results for each department are shown in table 8 below:

TABLE 8

Department(s)	C ₃	C′ ₃
			Department 1	100	5.05016
Department 2	29.702	1.5
			Department 3	4.03269	0.20366
Department 4	0	0
			Department 5	88.3985	4.46426
Department 6	0.93919	0.04743
			Department 7	0	0

4. Data application score (C) ₄ ) Calculation of

As shown in FIG. 2, the division gate performs the statistics of the time length of the average person data sharing platform, and then calculates the data application scores of the departments.

Illustratively, the shared data usage and data application score calculation results for each department are shown in Table 9 below:

TABLE 9

5. Shared deduction value (C ₅ ) Calculation of

As shown in FIG. 2, the deduction values of the departments are counted, and the shared deduction value of the departments is calculated through summation.

Illustratively, the deduction values for the respective cases are shown in the following table 10:

table 10

The data deduction page is shown in fig. 4.

6. Calculation of the composite score value (C)

As shown in fig. 2, the composite score of each department is calculated by applying a score average to the data scale score, the data quality score, the data value score and the data by an average method and then combining the shared deduction score.

The specific calculation formula is as follows:

C＝(C ₁ +C ₂ +C ₃ +C ₄ )/4-C ₅

exemplary, specific calculations are shown in table 11 below:

TABLE 11

Department(s)	C	C ₁	C ₂	C ₃	C ₄	C ₅
							Department 1	56.44	0.58	100.00	100.00	25.18	0.00
Department 2	77.0103	78.34	100.00	29.70	100.00	0.00
							Department 3	58	100.00	90.00	4.03	37.97	0.00
Department 4	38.0899	21.16	100.00	0.00	31.20	0.00
							Department 5	53.3777	6.27	100.00	88.40	18.84	0.00
Department 6	43.5314	47.82	100.00	0.94	25.37	0.00
							Department 7	42.3276	25.89	95.00	0.00	48.42	0.00

The system scoring page is shown in fig. 5.

The embodiment of the invention provides a complete calculation method in a breakthrough way, can calculate the total data sharing score (comprehensive score) of each data sharing participation department in the comprehensive traffic planning field, can provide objective data support for traffic industry data sharing evaluation work, assists in judging the behavior characteristics of each data co-building sharing main body, and can provide a basis for further stimulating sharing behaviors, optimizing sharing strategies and formulating sharing systems.

Corresponding to the data sharing scoring method in the transportation planning industry, the embodiment of the invention also provides a data sharing scoring device in the transportation planning industry, referring to a schematic structure diagram of the data sharing scoring device in the transportation planning industry shown in fig. 6, the device comprises:

an obtaining module 601, configured to obtain shared data information of a plurality of evaluation objects; the evaluation object comprises a department for implementing data co-construction sharing in the comprehensive traffic planning field;

the evaluation module 602 is configured to perform evaluation of five dimensions of data size, data quality, data value, data application and sharing deduction according to the shared data information of each evaluation object, so as to obtain a basic evaluation score of each evaluation object; the basic evaluation score comprises a data scale score, a data quality score, a data value score, a data application score and a sharing deduction score;

a determining module 603, configured to determine a total data sharing score of each evaluation object according to the basic evaluation score of each evaluation object.

Further, the shared data information includes data scale values of various types of shared data; the evaluation module 602 is specifically configured to:

According to the data scale values of various data corresponding to each evaluation object, calculating to obtain the conversion coefficient of each type of data by a threshold method;

and calculating to obtain the data scale score of each evaluation object according to the data scale value of each type of data corresponding to each evaluation object and the conversion coefficient of each type of data.

Further, the above-mentioned various data include space data, business data, apparatus data and statistical data; the evaluation module 602 is further configured to:

determining a comprehensive scale maximum value and a data scale maximum value of each type of data; the comprehensive scale maximum value is the maximum value in the data scale values of various data corresponding to each evaluation object; the maximum value of the data scale of each type of data is the maximum value in the data scale values of the type of data corresponding to each evaluation object;

and determining the ratio of the maximum value of the comprehensive scale to the maximum value of the data scale of each type of data as a conversion coefficient of each type of data.

Further, the shared data information includes data quality values of the shared data under a plurality of preset quality indexes; the evaluation module 602 is specifically configured to:

Further, the shared data information includes a first index value under a plurality of first value indexes corresponding to unconditional shared data of each data source and a second index value under a plurality of second value indexes corresponding to conditional shared data of each data source; the evaluation module 602 is specifically configured to:

According to first index values of unconditional shared data of various data sources corresponding to various evaluation objects under various first value indexes, calculating a conversion coefficient and a range of each first value index by a threshold method; according to the first index value of the unconditional shared data of various data sources corresponding to each evaluation object under each first value index, and the conversion coefficient and the range of each first value index, calculating to obtain a first score of each data source of each evaluation object corresponding to the unconditional shared data;

the data value score for each evaluation object is determined based on the first scores of the various data sources for the unconditionally shared data and the second scores of the various data sources for the conditionally shared data for the respective evaluation objects.

Further, the plurality of first price indicators include access number, download number, use frequency and space-time range; the plurality of second value indexes comprise application quantity, acquisition quantity, use frequency and space-time range; the evaluation module 602 is further configured to:

calculating to obtain an initial value score of each evaluation object according to a first score of each evaluation object corresponding to various data sources of unconditional shared data and a second score of each data source of conditional shared data, as well as a first adjustment coefficient corresponding to each first maximum value, each second maximum value, each data source of unconditional shared data and a second adjustment coefficient corresponding to each data source of conditional shared data;

Further, the shared data information includes total duration of use of shared data by the personnel of the department and accounting number of the department; the evaluation module 602 is specifically configured to:

according to the total using time length and the department accounting number corresponding to each evaluation object, calculating to obtain the average using time length corresponding to each evaluation object;

The implementation principle and the generated technical effects of the data sharing scoring device for the transportation planning industry provided by the embodiment are the same as those of the data sharing scoring method embodiment of the transportation planning industry, and for the sake of brief description, reference may be made to corresponding contents in the data sharing scoring method embodiment of the transportation planning industry.

As shown in fig. 7, an electronic device 700 provided in an embodiment of the present invention includes: the system comprises a processor 701, a memory 702 and a bus, wherein the memory 702 stores a computer program capable of running on the processor 701, when the electronic device 700 runs, the processor 701 and the memory 702 are communicated through the bus, and the processor 701 executes the computer program to realize the data sharing scoring method of the traffic planning industry.

Specifically, the memory 702 and the processor 701 can be general-purpose memories and processors, which are not particularly limited herein.

The embodiment of the invention also provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to execute the data sharing scoring method of the traffic planning industry in the previous method embodiment. The computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk, etc., which can store program codes.

Any particular values in all examples shown and described herein are to be construed as merely illustrative and not a limitation, and thus other examples of exemplary embodiments may have different values.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims

1. A data sharing scoring method for a transportation planning industry, comprising:

2. The method for scoring data sharing in a transportation planning industry according to claim 1, wherein the shared data information includes data scale values of various types of data shared; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

3. The method for scoring data sharing in the transportation planning industry according to claim 2, wherein the various types of data include spatial data, business data, equipment data and statistical data; the calculation of the conversion coefficient of each type of data by a threshold method according to the data scale value of each type of data corresponding to each evaluation object comprises the following steps:

4. The method for scoring data sharing in a transportation planning industry according to claim 1, wherein the shared data information includes data quality values of the shared data under a plurality of preset quality indexes; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

5. The method of data sharing scoring for a transportation planning industry of claim 4, wherein the plurality of quality metrics includes normalization, integrity, accuracy, consistency, and timeliness; the data quality value under normalization is the ratio of the number of elements in the data set meeting the normalization requirement to the number of elements in the data set being evaluated; the data quality value under the completeness is the ratio of the number of the elements in the assigned data set to the number of the elements in the assigned data set in the expected space-time range; the data quality value under the accuracy is the ratio of the number of elements in the data set meeting the data accuracy requirement to the number of elements in the data set to be evaluated; the data quality value under consistency is the ratio of the number of elements in the data set consistent with the historical data to the number of elements in the data set to be evaluated; the data quality value under timeliness is the ratio of the number of elements in the data set that meet the validity requirement to the number of elements in the data set that are evaluated.

6. The method for scoring data sharing in a transportation planning industry according to claim 1, wherein the shared data information includes a first index value under a plurality of first value indexes corresponding to unconditional shared data of each data source and a second index value under a plurality of second value indexes corresponding to conditional shared data of each data source; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

7. The method of data sharing scoring for a transportation planning industry of claim 6, wherein the plurality of first price indicators comprises a number of accesses, a number of downloads, a frequency of use, and a spatiotemporal range; the second value indexes comprise application quantity, acquisition quantity, use frequency and space-time range; the determining the data value score of each evaluation object according to the first score of each data source of the unconditional shared data and the second score of each data source of the conditional shared data, comprising:

8. The data sharing scoring method of the transportation planning industry according to claim 1, wherein the shared data information includes total duration of use of shared data by department personnel and the number of accounting persons by the department; and according to the shared data information of each evaluation object, performing evaluation of data scale, data quality, data value, data application and sharing deduction in five dimensions to obtain a basic evaluation value of each evaluation object, wherein the evaluation method comprises the following steps of:

9. A data sharing scoring device for a transportation planning industry, comprising:

10. An electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, wherein the processor, when executing the computer program, implements the data sharing scoring method of the transportation planning industry of any one of claims 1-8.