阅读说明：本技术 主数据获取方法及系统 (Main data acquisition method and system ) 是由 金雷 易韦宇 李春 于 2021-07-26 设计创作，主要内容包括：为了解决现有技术的不足,本发明提供一种主数据获取方法及系统,包括：根据目标场景的特点,获取主数据评价指标；构建模糊层次分析法模型,获得主数据识别模型；将目标场景下的数据通过所述主数据识别模型进行识别,获取所述目标场景下的主数据。本发明所述的方法根据目标场景的特点,获取主数据评价指标,然后在构建模糊层次分析法模型的基础上,获得主数据识别模型；最后将目标场景下的数据通过所述主数据识别模型进行识别,获取所述目标场景下的主数据。(In order to solve the defects of the prior art, the invention provides a method and a system for acquiring main data, which comprises the following steps: acquiring a main data evaluation index according to the characteristics of a target scene; constructing a fuzzy analytic hierarchy process model to obtain a main data identification model; and identifying the data in the target scene through the main data identification model to obtain the main data in the target scene. The method comprises the steps of obtaining a main data evaluation index according to the characteristics of a target scene, and then obtaining a main data identification model on the basis of constructing a fuzzy analytic hierarchy process model; and finally, identifying the data in the target scene through the main data identification model to obtain the main data in the target scene.)

1. A master data acquisition method, comprising:

acquiring a main data evaluation index according to the characteristics of a target scene;

constructing a fuzzy analytic hierarchy process model to obtain a main data identification model;

and identifying the data in the target scene through the main data identification model to obtain the main data in the target scene.

2. The method for acquiring master data according to claim 1, wherein the "master data evaluation index" includes:

sharing, stability, criticality, originality, sustainability, irreplaceability, broad applicability.

3. The method for acquiring main data according to claim 1, wherein the constructing a fuzzy analytic hierarchy process model to obtain a main data identification model comprises:

constructing a fuzzy consistent judgment matrix to obtain a fuzzy complementary judgment matrix;

solving the weight of the fuzzy complementary judgment matrix by using a weight solving formula;

and after consistency check is carried out, the main data identification model is obtained.

4. The method according to claim 3, wherein the step of constructing the fuzzy consistent judgment matrix to obtain the fuzzy complementary judgment matrix comprises:

setting variables:

X＝{x₁,x₂,x₃,...,x₇in which x₁"shareability", x₂"stability", x₃X is "Critical₄"originality", x₅"sustainability", x₆"Unsubstitutable", x₇"wide applicability";

y is "whether it is main data of a highway";

each time X is taken as { X ═ X₁,x₂,x₃,...,x₇Two evaluation indexes x in_iAnd x_jTwo by two, according to x_iAnd x_jEstablishing a fuzzy complementary judgment matrix A (a) for the relative importance degree of the dependent variable y_ij)_7×7Wherein a is_ijX is obtained according to a 0.1-0.9 scaling method_iAnd x_jRelative importance ratio to y;

the fuzzy complementary judgment matrix A is obtained as follows:

5. the master data acquisition method according to claim 3, wherein said "consistency check" comprises:

and carrying out consistency check on the weights by using the compatibility of the fuzzy judgment matrix and the attitude of a decision maker.

6. The master data acquisition method according to claim 5, comprising:

if fuzzy complementation judging matrix A and characteristic matrix Z^*Compatibility of (A) with (B) is satisfied^*) When the weight vector is less than or equal to T, the solved weight vector is considered to be consistent with the fuzzy complementary judgment matrix; wherein, T is attitude of the decision maker.

7. Use of the main data acquisition method according to claims 1-6 in the main data acquisition direction of a highway.

8. The utility model provides an wisdom highway owner data acquisition system which characterized in that includes:

the acquisition module is connected with an external acquisition device and used for acquiring highway data;

the processing unit is connected with the acquisition module and used for acquiring a main data identification model and identifying the highway data according to the main data identification model so as to acquire the highway main data;

the storage module is connected with the processing unit and used for storing the main data of the highway;

the compiling module is connected with the storage module and the processing unit and is used for standardizing the main data of the highway according to main data evaluation indexes to obtain standardized main data of the highway;

the processing unit is connected with the storage module and the compiling module and is used for storing the standardized main data of the highway.

9. An electronic device for obtaining a main data system of an intelligent highway, comprising:

a storage medium for storing a computer program;

a processing unit in data exchange with the storage medium for performing the steps of the main data acquisition method according to any one of claims 1 to 6 by the processing unit executing the computer program when acquiring the intelligent highway main data.

10. A computer-readable storage medium characterized by:

the computer readable storage medium having stored therein a computer program;

the computer program, when executed, performs the steps of the master data acquisition method as claimed in any one of claims 1 to 6.

Technical Field

The invention relates to the technical field of intelligent transportation, in particular to a method, a system, an electronic device and a computer readable storage medium for acquiring main data of an intelligent highway.

Background

At present, on the basis of combining respective industrial characteristics and deeply researching the system of the group, some companies respectively design and realize a unique data coding platform or a main data management system in the industries such as power grids, automobiles, petroleum, steel, insurance, communication, tobacco, railways and the like, and the data management level of enterprise owners is improved. Therefore, the invention combines the functional requirements of the A provincial intelligent highway business system to research an effective method for the standardized construction of main data of the highway, and lays a solid foundation for the efficient management of the main data of the highway.

Nowadays, with the accelerated construction of highways, the rapid increase of services and the updating and upgrading of technologies, the total amount of highway data resources is greatly increased, and a prerequisite is provided for highway-oriented big data application.

At present, the research on main data of the highway in the industry is immature, a complete main data management flow of the highway is not formed, and the establishment of a main data standard is a premise for realizing the management of the main data; therefore, to realize the large-scale application of the big data technology on the highway, it is urgently needed to identify the main data from huge highway data resources and establish a highway main data standard system to ensure the uniformity and the normalization of the highway main data and provide a good data basis for the wide application of the big data in the highway scene.

Disclosure of Invention

In order to solve the deficiencies of the prior art, the present invention provides a method and a system for acquiring master data, which are used for solving at least one technical problem in the background art.

The technical scheme adopted by the invention is as follows:

a master data acquisition method, comprising:

acquiring a main data evaluation index according to the characteristics of a target scene;

constructing a fuzzy analytic hierarchy process model to obtain a main data identification model;

and identifying the data in the target scene through the main data identification model to obtain the main data in the target scene.

The main data evaluation index comprises:

sharing, stability, criticality, originality, sustainability, irreplaceability, broad applicability.

The method for constructing the fuzzy analytic hierarchy process model and obtaining the main data identification model comprises the following steps:

constructing a fuzzy consistent judgment matrix to obtain a fuzzy complementary judgment matrix;

solving the weight of the fuzzy complementary judgment matrix by using a weight solving formula;

and after consistency check is carried out, the main data identification model is obtained.

The step of constructing the fuzzy consistent judgment matrix to obtain the fuzzy complementary judgment matrix comprises the following steps:

setting variables:

X＝{x₁,x₂,x₃,...,x₇in which x₁"shareability", x₂"stability", x₃X is "Critical₄"originality", x₅"sustainability", x₆"Unsubstitutable", x₇"wide applicability";

y is "whether it is main data of a highway";

each time X is taken as { X ═ X₁,x₂,x₃,...,x₇Two evaluation indexes x in_iAnd x_jTwo by two, according to x_iAnd x_jEstablishing a fuzzy complementary judgment matrix A (a) for the relative importance degree of the dependent variable y_ij)_7×7Wherein a is_ijX is obtained according to a 0.1-0.9 scaling method_iAnd x_jRelative importance ratio to y;

the fuzzy complementary judgment matrix A is obtained as follows:

the consistency check comprises the following steps:

and carrying out consistency check on the weights by using the compatibility of the fuzzy judgment matrix and the attitude of a decision maker, wherein the consistency check comprises the following steps:

if fuzzy complementation judging matrix A and characteristic matrix Z^*Compatibility of (A) with (B) is satisfied^*) When the weight vector is less than or equal to T, the solved weight vector is considered to be consistent with the fuzzy complementary judgment matrix; wherein, T is attitude of the decision maker.

The application of the main data acquisition method in the main data acquisition direction of the expressway is disclosed.

An intelligent highway main data acquisition system comprising:

the acquisition module is connected with an external acquisition device and used for acquiring highway data;

the processing unit is connected with the acquisition module and used for acquiring a main data identification model and identifying the highway data according to the main data identification model so as to acquire the highway main data;

the storage module is connected with the processing unit and used for storing the main data of the highway;

the compiling module is connected with the storage module and the processing unit and is used for standardizing the main data of the highway according to main data evaluation indexes to obtain standardized main data of the highway;

the processing unit is connected with the storage module and the compiling module and is used for storing the standardized main data of the highway.

An electronic device for obtaining a smart highway master data system, comprising:

a storage medium for storing a computer program;

and the processing unit is used for exchanging data with the storage medium and executing the computer program through the processing unit when the intelligent expressway main data is acquired so as to perform the steps of the main data acquisition method.

A computer-readable storage medium having a computer program stored therein;

the computer program, when running, performs the steps of the master data acquisition method as described above.

The invention has the beneficial effects that:

the method comprises the steps of obtaining a main data evaluation index according to the characteristics of a target scene, and then obtaining a main data identification model on the basis of constructing a fuzzy analytic hierarchy process model; and finally, identifying the data in the target scene through the main data identification model to obtain the main data in the target scene.

The system provided by the invention utilizes the data exchange between the processing unit and the storage module to identify the acquired highway data, thereby achieving the purpose of acquiring the main data of the highway.

Drawings

FIG. 1 is a flow chart of the method of the present invention;

fig. 2 is a block diagram of the system of the present invention.

Detailed Description

The method steps described in the present invention are shown in fig. 1, and an embodiment is provided in this section:

in order to solve the problems in the prior art, the method for acquiring main data of the intelligent highway specifically comprises the following steps when the method is applied to the acquisition of the main data of the intelligent highway:

according to the functional requirement analysis of the highway toll system and the combination of the data characteristics, the important data of the highway toll system, such as the following table 1, and the important data of the highway vehicle-road cooperation system, such as the following table 2, are summarized. The wisdom communication station mainly has six functional module, is respectively: the system comprises a cloud platform interaction function, a roadside device interaction function, a video analysis function, a data processing function, an intelligent communication station configuration management function and an intelligent communication station interaction function, wherein the specific functions corresponding to all modules are shown in the following table 3;

(1) selecting main data evaluation index of highway

Judging whether a certain data can be used as the main data of the highway or not according to the definition of the main data of the highway and the characteristics of the data of the highway service system, and emphasizing on considering the following factors:

(ii) sharing Property

The main data needs to be shared among a plurality of service systems, and for a certain service data, the larger the sharing requirement of each service system on the service system, the more important the service system is.

(ii) stability

The main data transmits important information among the service systems, so that the attributes of the main data cannot be changed frequently and are kept stable.

③ Key

The main data should have a high business value, and it should be able to describe the core business entity and play a key role in completing the business process and implementing the functions of the business system.

Originality

The main data is important basic data, and the basic data can be directly extracted from a business system without secondary processing and original attribute characteristics.

Sustainability

Sustainability is distinguished from stability, sustainability means that main data in a business system has a long lasting life cycle, and whether data attributes or characteristics are changed or not is not reflected, and main data containing important business information can be stored in the business system for a period of time so as to be accessed and used by each system.

Sixth irreplaceability

The main data can uniquely identify the core business entity, and repeated main data can not appear among systems, and ambiguity can not be generated.

Wide applicability

The wide applicability is distinguished from the shareability, and the wide applicability means that the main data can be used in a multi-level range in various service scenes, such as a group monitoring center to a road section monitoring branch center of a monitoring scene, a provincial networking center to an area/road section branch center of a charging scene, and the like.

Determining the evaluation indexes of the main data of the expressway as follows according to the analyzed main data identification standard of the expressway: seven items of sharing, stability, criticality, originality, sustainability, non-substitutability, and general usability, see table 4.

(2) Establishing a model of a fuzzy analytic hierarchy process

Firstly, constructing a fuzzy consistent judgment matrix

Provided with a matrix A ═ (a)_ij)_n×nIf the following conditions are met:

0≤a_ij≤1,(i＝1,2,...,n；j＝1,2,...,n) (1)

then a is called the blur matrix.

If the fuzzy matrix A ═ a_ij)_n×nSatisfies the following conditions:

a_ij+a_ji＝1,(i＝1,2,...,n；j＝1,2,...,n) (2)

the fuzzy matrix a is called fuzzy complementary matrix.

If fuzzy complementary matrix A ═ a_ij)_n×nSatisfies the following conditions:is provided with

a_ij＝a_ik-a_jk+0.5 (3)

The fuzzy matrix a is called a fuzzy consistent matrix.

In the fuzzy analytic hierarchy process, business field experts are required to compare the relative importance of each evaluation index pairwise, the quantitative representation is carried out by a 0.1-0.9 scale method, and the scale and the meaning are shown in a table 5, so that the consistency of thinking can be ensured.

It is worth noting that the expert scoring process is a key for determining the index weight, and the comparison result between two evaluation indexes may be inconsistent due to the influence of subjective factors, so that the influence on the determination of the final weight is a certain limitation of the method. The invention integrates the opinions of three service experts to form a scoring result, and reduces the irrational influence of the subjective tendency of a single expert on the scoring result.

Comparing and grading each evaluation index by experts to obtain a fuzzy complementary judgment matrix A ═ (a)_ij)_n×nThe fuzzy complementary judging matrix satisfies the formula (2) and the formula (4).

a_ij＝0.5，i＝1，2，3，...，n(4)

According to the operation flow, the following fuzzy complementary judgment matrix is established:

firstly, defining seven evaluation indexes of the expressway as seven variables respectively:

X＝{x₁,x₂,x₃,...,x₇in which x₁"shareability", x₂"stability", x₃X is "Critical₄"originality", x₅"sustainability"，x₆"Unsubstitutable", x₇"wide applicability". Let y be "whether it is main data of highway", take X ═ X each time₁,x₂,x₃,...,x₇Two evaluation indexes x in_iAnd x_jTwo by two, according to x_iAnd x_jEstablishing a fuzzy complementary judgment matrix A (a) for the relative importance degree of the dependent variable y_ij)_7×7Wherein a is_ijX is obtained according to a 0.1-0.9 scaling method_iAnd x_jRelative importance ratio to y. The fuzzy complementary judgment matrix A is obtained as follows:

calculating weight formula

According to the experience of the predecessor, a general calculation formula for solving the weight of the fuzzy complementary judgment matrix is obtained through derivation. The weight solving formula is simple and efficient, the calculated amount is small, and the practical operation is convenient. The calculation formula is as follows:

checking consistency

People know the actual problems one-sidedly, understand the deviation, or the complexity of the problems causes difficulty to a certain extent for people to understand, so that the judgment matrix is often inconsistent in constructing the judgment matrix, and the judgment matrix cannot be used for analyzing and researching the actual problems if the judgment matrix does not conform to the normal thinking habit of human beings. Therefore, after the weight is obtained according to the formula (6), whether the weight meets the consistency requirement is judged, and the irrational influence of the subjective unstable factors on the evaluation result is avoided. It has been proposed to use the compatibility of the calculated fuzzy decision matrix as a consistency check. It has also been proposed to adjust the consistency by blurring the essential conditions of the consistency matrix.

Let matrix A ═ a_ij)_n×nAnd matrix B ═ B_ij)_n×nAre all fuzzy judgment matrixes, scales

Is an index of compatibility between A and B.

In addition, the weight vector of the fuzzy complementary matrix A can be solved according to the weight solving formula (6), and Z ═ can be set₁,Z₂,...,Z_n)^TIs the weight vector of the fuzzy decision matrix a, Z generally satisfies equation (8).

Order toThen called n-order matrix

Z^*＝(Z_ij)_n×n (9)

Is the feature matrix of the fuzzy complementary judging matrix A.

When consistency check is carried out on the obtained weights by using the compatibility of the fuzzy judgment matrix, the attitude of a decision maker needs to be referred. For attitude T of the decision maker, if fuzzy complementation is adopted, the judgment matrix A and the feature matrix Z are judged^*Compatibility of (A) with (B) is satisfied^*) And when the weight vector is less than or equal to T, the solved weight vector is considered to be consistent with the fuzzy complementary judgment matrix. Wherein T is a constant, and the smaller T is, the higher the consistency requirement of a decision maker on the fuzzy judgment matrix is, and T is 0.2 according to the requirement of main data of the highway and the expert opinion.

Fourthly, obtaining a main data identification model of the highway

The weight vector is calculated through the steps, and when the weight vector passes consistency check, the main data identification model of the expressway can be determined:

y＝XZ＝z₁x₁+z₂x₂+...+z₇x₇ (10)

whereinz_iI ∈ {1,2, 3., 7} is a weight value solved by equation (6), x_iI e {1,2, 3.. 7} represents δ for any highway traffic entity_kK ∈ {1,2, 3., n } corresponds to the ith highway primary data rating score, if δ_kIf the requirement of the ith index of main data evaluation of the highway is met, x_i1, otherwise x_i＝0.

(3) Identifying highway business system data

And (3) respectively substituting the highway service system data into the recognition model according to the highway main data recognition model obtained in the previous step to calculate the score, setting the threshold of the main data of each scene of the highway according to the requirement of the highway main data and expert opinions, and obtaining the highway service data with the calculated result score larger than the set threshold, namely the main data of the scene.

Specific example II: the main data identification of the invention is carried out by taking the expressway in province A as an example.

Firstly, analyzing key business of expressway operation in province A, and obtaining a fuzzy complementary judgment matrix A by combining business expert opinions as follows:

calculating the matrix A according to the formula (6), and obtaining a weight vector Z of each evaluation index as follows:

Z＝(0.167,0.150,0.152,0.117,0.133,0.148,0.133) (12)

calculating a feature matrix Z of the weight vector Z from the weight vector Z according to equation (9)^*Comprises the following steps:

then according to the obtained weight vector feature matrix Z^*Performing compatibility judgment with the fuzzy complementary judgment matrix, and obtaining I (A, Z) by formula (7)^*) 0.132 is not more than 0.2, which meets the compatibility judgment,the fuzzy judgment matrix is shown to pass the consistency check.

The weight vector Z (12) of each evaluation index can be obtained, and a fuzzy analytic hierarchy process scoring model for main data identification of the expressway is as follows:

y＝XZ＝0.167x₁+0.150x₂+0.152x₃+0.117x₄+0.133x₅+0.148x₆+0.133x₇ (14)

analyzing the key business of the expressway operation of province A, judging whether each item of obtained business data meets seven evaluation indexes of main data of the expressway, and if the data meets the ith evaluation index, corresponding x_iThe value is 1, otherwise, 0, so that the evaluation index vector X of each item of service data is obtained as { X ═ X }₁,x₂,x₃,...,x₇}. The scores of all the service data are obtained according to the main data requirements of the highway and by combining the review opinions of service experts, as shown in appendix B, and the corresponding evaluation index vectors are obtained as shown in Table 6.

And substituting the evaluation index vector X of the business data into a formula (14) according to a main data scoring model of the highway for calculation, and sequencing according to the score values of the business data from large to small, wherein the result is shown in table 3. And then setting the threshold value for judging the main data of the highway to be 0.8 according to the main data requirement of the highway and combining the opinions provided by experts, and obtaining the main data of the highway as follows: route data, road segment data, facility data, equipment data, interworking node data, as in table 7.

The invention also discloses an embodiment:

as shown in fig. 2, an intelligent highway main data acquisition system includes: the system comprises an acquisition module 100, a processing unit 200, a storage module 300 and a compiling module 400; the acquisition module 100 is connected with an external acquisition device and used for acquiring highway data; the processing unit 200 is connected to the acquisition module 100, and is configured to obtain a main data identification model and identify the highway data according to the main data identification model to obtain highway main data; the storage module 300 is connected with the processing unit 200 and is used for storing the main highway data; the compiling module 400 is connected with the storage module 300 and the processing unit 200, and is configured to standardize the main highway data according to a main data evaluation index to obtain standardized main highway data; the processing unit 200 is connected to the storage module 300 and the compiling module 400, and is configured to store the standardized main highway data.

Data encoding is to assign a specific symbol to a certain kind of information to meet the needs of actual services. Data coding mainly considers the following principles: uniqueness, rationality, expandability, simplicity, applicability, and normalization. For the authoring module 400: after the main data of the highway is identified, the main data is classified and subjected to attribute coding according to relevant regulations, industry standards and the like, so that the main data standard of the highway is established.

The main data is used as important basic data in the service information system, high consistency of the main data is ensured in the system and among the systems, and by establishing a uniform main data coding standard, irregularity and comprehension ambiguity under natural language description can be avoided, so that information processing of a computer is facilitated, and a language basis for intercommunication and sharing is provided among the service systems, so that the efficiency of data sharing application is improved.

The invention also provides an embodiment:

a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the method as set out above. The computer program may be downloaded and installed from a network. The computer program, when executed by the CPU, performs the above-described functions defined in the system of the present invention.

The invention also provides an embodiment:

a computer-readable storage medium having a computer program stored therein; the computer program, when running, performs the steps of the master data acquisition method as described above.

In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The intelligent expressway main data standard is compiled by referring to national and industrial standards and technical specifications on the basis of analyzing and combing the functional requirements of various main business scenes of the intelligent expressway, researching important data and types required by three main scene functions of intelligent expressway monitoring, charging and vehicle-road cooperation, establishing a main data identification method and model based on a fuzzy analytic hierarchy process, identifying the main data of the expressway, and finally forming the main data standard of the intelligent expressway.

The above-mentioned invention numbers are merely for description and do not represent the merits of the implementation scenarios.

Attached table:

table 1 charging system important data table

Table 2 important data table of vehicle-road cooperative system

TABLE 3 Intelligent communication station function List

TABLE 4 seven major evaluation indexes of main data of expressway

TABLE 50.1-0.9 Scale method and significance thereof

Table 6 evaluation index vector table for important highway service data

Table 7 main data identification evaluation score table for expressway

The above disclosure is only a few specific implementation scenarios of the present invention, however, the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.