Infrastructure master data element determination method for road full life cycle

Abstract

The embodiment of the invention discloses an infrastructure main data element determination method for the full life cycle of a road. The method comprises the following steps: acquiring a plurality of business scenes and business data in the full life cycle of the road; according to the source and the use condition of each piece of service data, constructing a data-flow matrix used for representing the data flow of the service data in each service scene; screening a plurality of business main data of which the data flow meets the source, utilization and storage requirements; according to the first correlation coefficient between each piece of business data and each piece of infrastructure data and the second correlation coefficient between each piece of business data, constructing a joint matrix used for representing a dual correlation relationship between the business data and the infrastructure data; and according to the joint matrix and a main data screening principle, determining an infrastructure main data element by taking the service main data as a unit. According to the embodiment, the main data elements of the infrastructure are reasonably determined from a business scene.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of highway master data identification, and in particular to a method for determining infrastructure master data elements for the entire life cycle of a highway. Background Art

[0002] Highway infrastructure involves a large number of types and has a long life cycle from early design and construction to later maintenance and operation, road administration, etc. Highway infrastructure data refers to the data that can be generated or required by highway infrastructure, and is the main support for highway business.

[0003] Since businesses at different stages have different requirements for the format and accuracy of infrastructure data, the current highway infrastructure data is repeatedly collected and stored separately, which is time-consuming, labor-intensive and difficult to maintain. This can easily lead to "data islands" and is not conducive to the integration of infrastructure data throughout the entire life cycle. Summary of the invention

[0004] The embodiment of the present invention provides a method for determining infrastructure master data elements for the entire life cycle of a highway, reasonably determines infrastructure master data elements based on business scenarios, and promotes interactive sharing of infrastructure data among different business scenarios through a master data base.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining infrastructure master data elements for the entire life cycle of a highway, including:

[0006] Obtain multiple business scenarios throughout the life cycle of a highway, as well as the infrastructure-related business data that can be produced and required by each business scenario;

[0007] According to the source and usage of each business data, a data-process matrix is ​​constructed to characterize the data flow of business data in each business scenario;

[0008] According to the data-process matrix, select various business master data whose data flows meet the requirements of source, utilization and storage;

[0009] Constructing a joint matrix for representing the dual association relationship between the business data and the infrastructure data according to the first association coefficient between each business data and each infrastructure data, and the second association coefficient between each business data;

[0010] According to the joint matrix and the master data screening principle, infrastructure master data elements are determined based on business master data, wherein the infrastructure data element is the smallest data unit of infrastructure data.

[0011] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0012] one or more processors;

[0013] a memory for storing one or more programs,

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining infrastructure master data elements for the entire life cycle of a highway as described in any embodiment.

[0015] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining infrastructure master data elements for the entire life cycle of a highway as described in any embodiment.

[0016] In summary, the embodiment of the present invention provides a method for determining infrastructure master data elements for the entire life cycle of a highway. According to the data requirements of typical businesses in the entire life cycle of a highway, the association relationship between business data and infrastructure data, as well as the association relationship between business data, reasonably determine the highway infrastructure master data throughout the entire life cycle, and promote the interactive sharing of infrastructure data in different business scenarios. Specifically, the method first constructs a data-process matrix to characterize the data flow of business data in each business scenario according to the source and usage of business data, and selects business master data that meets the source, utilization and storage requirements; then, based on the business master data, the infrastructure data directly associated with the business master data and the infrastructure data indirectly associated with other business data are determined respectively, and the infrastructure data elements corresponding to the current business master data are determined by the two associated infrastructure data. The whole method uses business master data as a bridge, integrates the association relationship between business data and infrastructure data, and the association relationship between business data into the screening of infrastructure master data elements, and can further improve the application value of infrastructure master data elements in business scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of a method for determining infrastructure master data elements for the entire life cycle of a highway provided by an embodiment of the present invention.

[0019] Figure 2It is a data-process matrix diagram of business data related to infrastructure in a full life cycle business scenario provided by an embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of a joint matrix including business data correlation coefficients and business data and infrastructure data correlation coefficients provided by an embodiment of the present invention.

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0023] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0024] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Figure 1 1 is a flowchart of a method for determining infrastructure master data elements for the entire life cycle of a highway provided by an embodiment of the present invention. The method is executed by an electronic device, such as Figure 1 As shown, the method specifically includes:

[0026] S110. Acquire multiple business scenarios in the entire life cycle of a highway, as well as business data related to infrastructure that can be produced and required by each business scenario.

[0027] The business data here refers to a series of data categories divided from a business perspective, such as topography, route conditions, etc., rather than specific data values. This embodiment sorts out highway infrastructure data through business scenarios and business data based on the application characteristics of highway data.

[0028] Optionally, the business scenarios in the entire life cycle of a highway can be divided into primary business scenarios and secondary business scenarios. The primary business scenarios are the major categories of business scenarios divided according to the basic stages of the entire life cycle of a highway, which may include highway construction scenarios, highway maintenance scenarios, and highway operation scenarios. The secondary business scenarios are business scenarios obtained by further subdividing the primary business scenarios. For example, by subdividing the highway construction scenarios, secondary business scenarios such as surveying, design, and construction are obtained; by subdividing the highway maintenance scenarios, secondary business scenarios such as road condition inspection and assessment, maintenance decision-making, maintenance design, and maintenance construction are obtained; by subdividing the highway operation scenarios, secondary business scenarios such as operation monitoring and emergency response are obtained.

[0029] Optionally, the business data includes data that can be provided by each business scenario and data required by each business scenario. This embodiment focuses on data related to highway infrastructure in the business data, such as topography, line conditions, design information, construction delivery data, etc.

[0030] In practical applications, the above business scenarios and business data can be determined manually based on experience, or automatically determined based on the business system in the entire life cycle of the highway. When automatically determined based on the business system, this embodiment provides the following specific implementation method:

[0031] First, multiple first-level business scenarios are determined based on the main functions of multiple business systems in the entire life cycle of the highway. For example, the business systems involved in the entire life cycle of the highway include the highway construction system, highway maintenance system, and kilometer operation system. The system name or system description can be read to understand the main functions of each business system, and the main functions can be summarized into each first-level business scenario. The specific understanding and summarization operations can be achieved with the help of natural language processing technology, which will not be repeated here.

[0032] Then, according to the functional modules related to infrastructure in each business system, the secondary business scenarios included in each primary business scenario are determined. Taking the highway maintenance system as an example, the functional modules related to infrastructure in this system include road condition inspection and assessment module, maintenance decision module, maintenance design module and maintenance construction module, etc. Then, the names or descriptions of these modules can be read to understand the business content of each functional module, and these business contents can be summarized as the secondary business scenarios under the highway maintenance scenario.

[0033] Next, crawl the business data related to infrastructure required by each secondary business scenario from the input file collection of each functional module. Taking the design scenario as an example, the secondary business scenario comes from the design module in the highway construction system. Then extract the input file collection of the module background, and crawl the file names or file descriptions related to infrastructure from the input file collection, and use the themes of these file names or file descriptions as the business data required by the design scenario, including topography.

[0034] Similarly, the business data related to infrastructure that can be produced by each secondary business scenario can be crawled from the output file collection of each functional module. Taking the design scenario as an example, the output file collection of the design module is extracted, and the file names or file descriptions related to infrastructure are crawled from the output file collection, and these file names or file descriptions are summarized as business data that can be produced by the design scenario, including line conditions, design information, etc.

[0035] S120. Construct a data-process matrix for characterizing the data flow of the business data in each business scenario according to the source and usage of each business data.

[0036] For certain business data, the source of the data refers to the business scenarios that can produce the business data. The usage of the data includes the time span between the business scenarios that use the business data, and the frequency of use of the business data by each business scenario.

[0037] This embodiment comprehensively considers the source and usage of business data, constructs a matrix with business data as rows and business scenarios as columns, and uses the role of a business scenario in the data flow of a certain business data as a matrix element to characterize the data flow of infrastructure-related business data in the business scenario of the entire life cycle of the highway (referred to as data flow). This embodiment calls this matrix a data-process matrix. Among them, the role played by a certain business scenario in the data flow of a certain business data includes producers (C), short-term users (SU), medium-term users (MU) and long-term users (LU), which are also the four element values ​​of the data-process matrix, such as Figure 2 In a specific implementation, the construction of the matrix may include the following steps:

[0038] Step 1: Build an empty data-process matrix with each business data as row and each secondary business scenario as column. Figure 2 First, we construct a data-process matrix with empty elements. Each row of the matrix corresponds to a business data u related to infrastructure, and each column corresponds to a secondary business scenario. All rows of u together constitute a business data set related to infrastructure.

[0039] Step 2: Perform the following operations for each row of business data:

[0040] S1-1. Assign the element corresponding to the secondary business scenario that can produce the current business data to the producer. Figure 2 In this matrix, C is used to represent the producer. Taking the line condition of the second row of business data as an example, the secondary business scenario that generates the business data is the design scenario, and the matrix element in the second row and second column is assigned C.

[0041] S1-2, assign the element corresponding to the secondary business scenario that requires the current business data to the user. Figure 2 , using U to represent the user, still taking the line condition of the second row of business data as an example, the secondary business scenarios using this business data include construction scenarios, maintenance design scenarios, maintenance construction scenarios, and emergency response scenarios, then the matrix elements in the second row with these scenarios as columns are all assigned the value of U. It should be noted that this step is an intermediate step, Figure 2 The intermediate matrix including the element U is not shown.

[0042] S1-3. Modify the users who belong to the same primary business scenario as the producer to short-term users. Since the same business scenario corresponds to the same life stage in the entire life cycle of a highway, the time span of the secondary business scenarios belonging to the same primary business scenario is relatively short. Based on this rule, in this embodiment, if the business data produced by a secondary business scenario is used by other secondary business scenarios under the same primary business scenario, such use will be regarded as short-term use. Still refer to Figure 2 SU is used in the matrix to represent short-term users. Taking the second row of business data as an example, the U elements in the second row of the above intermediate matrix are verified one by one. If the secondary business scenario corresponding to the U element and the C element in the second row belongs to the same primary business scenario, the U element is modified to SU.

[0043] S1-4. Users who belong to different first-level business scenarios from the producer and whose frequency of use of the current business data is less than the set threshold will be modified to medium-term users; users who belong to different first-level business scenarios from the producer and whose frequency of use of the current business data is greater than or equal to the set threshold will be modified to long-term users. Since different first-level business scenarios correspond to different life stages in the entire life cycle of a highway, the time span between the second-level business scenarios under different first-level business scenarios is relatively large. Based on this rule, in this embodiment, if the business data generated by a certain second-level business scenario is used by the second-level business scenarios under other first-level business scenarios, such use will be regarded as medium- and long-term use, and the medium-term use and long-term use will be further distinguished by the frequency of use threshold. Optionally, the threshold can be 5 times / day. Still referring to Figure 2 MU and LU are used in the matrix to represent medium-term users and long-term users respectively. Taking the second row of business data as an example, after the operation of S1-3, the elements corresponding to the secondary business scenarios maintenance design, maintenance construction and emergency disposal are still U. These U elements are verified one by one. Among them, if the frequency of use of the maintenance design scenario for line conditions is greater than 5 times / day, the U element of this column is modified to LU. If the frequency of use of the maintenance construction scenario and the emergency disposal scenario for line conditions is less than 5 times / day, the U elements of these two columns are modified to MU.

[0044] S130. According to the data-process matrix, screen the business master data whose data flows meet the requirements of source, utilization and storage.

[0045] This embodiment screens the master data in the business data from the perspective of data usage. The specific screening method can be cleverly integrated with the element characteristics of the data-process matrix, and a simple count can be used to determine whether the business data meets the master data conditions. Optionally, the business master data must meet at least one of a single source, high availability, and stable storage.

[0046] Among them, single source means that each business data is allowed to have one producer. Combined with the data-process matrix, the business data that meets the single source requirement can be screened by the number of producer elements in each row, that is, there is only one C in each row of the data-process matrix.

[0047] High utilization means that the business data has a high utilization rate. In combination with the data-process matrix, the business data that meets the high utilization can be screened by counting the LU, MU and SU. For example, if a row of the data-process matrix includes 1 LU, or 2 MUs, or more than 2 SUs, the business data in this row is screened as business data that meets the high utilization. Furthermore, the screening condition can be determined manually as needed, or it can be calculated and determined based on the usage of the business data by the three categories of users. When the usage of the business data by the three categories of users is calculated and determined, this embodiment provides the following specific implementation method:

[0048] Step 1: According to the time span between the three types of users and producers and the frequency of use of business data, each user element in the data-process matrix is ​​converted into a count value with short-term users as the counting unit.

[0049] Optionally, since the longer the time span between the user and the producer is, the higher the effective utilization of the current service data is, the more effective utilization of the service data that can be reflected by the MU is higher than that of the SU. In order to quantify the difference between the MU and the SU, this embodiment converts each MU into a SU according to the minimum time span t1 between the SU and the C and the minimum time span t2 between the MU and the C. SUs, That is, the count value with short-term users as the counting unit. Since t1>t2, Furthermore, t1 and t2 can be determined in the following manner: arrange each first-level business scenario in chronological order, and calculate the difference between the central time points of each two adjacent first-level business scenarios to obtain the time span between each two adjacent first-level business scenarios; average the time spans between all adjacent first-level business scenarios to obtain the average time span between the first-level business scenarios, which represents the minimum time span t1 between MU and C. Similarly, based on the above arrangement, further arrange the second-level business scenarios under each first-level business scenario in chronological order to obtain the time series of the second-level business scenarios; calculate the difference between the central time points of each two second-level business scenarios to obtain the time span between each two adjacent second-level business scenarios; average the time spans between all adjacent second-level business scenarios to obtain the average time span between the second-level business scenarios, which represents the minimum time span t2 between SU ​​and C.

[0050] In addition, since the higher the frequency of service data use by the user, the higher the effective utilization of the current service data, the more effective utilization of service data that can be reflected by LU is higher than that that can be reflected by MU. In order to quantify the difference between LU and MU, this embodiment converts each LU into an average service data use frequency f1 of LU and an average service data use frequency f2 of MU according to the average service data use frequency f1 of LU and the average service data use frequency f2 of MU. MU, which is then converted to SUs, That is, the count value with short-term users as the counting unit. Since f1>f2, Furthermore, the usage frequency of the business data in the row of all LUs in the data-flow matrix can be counted, and the usage frequency of all LUs can be averaged to obtain the average usage frequency f1 of LU for business data; at the same time, the usage frequency of the business data in the row of all SUs in the data-flow matrix can be counted, and the usage frequency of all MUs can be averaged to obtain the average usage frequency f2 of MU for business data.

[0051] Step 2: Filter the service data that meets the high utilization requirement based on the sum of the count values ​​of each row. Through step 1, the LU and MU of each row are converted into the count value of SU, and the count value of the SU element itself is 1. Add the count values ​​in the same row, and the service data corresponding to the row whose sum of the count values ​​is greater than the set threshold is the service data that meets the high utilization requirement. Preferably, the set threshold is 3.

[0052] Stable storage means that business data has the characteristics of long-term stable storage. In combination with the data-process matrix, business data that meet the requirements of stable storage can also be screened by counting LUs and MUs. For example, if a row of the data-process matrix includes 2 LUs, or 4 MUs, or 1 LU and 2 MUs, the business data in this row is screened as business data that meet the requirements of long-term stable storage. Similarly, the screening conditions can be determined manually as needed, or can be calculated and determined based on the usage of business data by medium- and long-term users. When calculated and determined based on the usage of business data by medium- and long-term users, this embodiment provides the following specific implementation method:

[0053] Step 1. According to the frequency of use of business data by mid-term users and long-term users, convert each mid-term user element and long-term user element in the data-process matrix into a count value with mid-term users as the counting unit. Since stable storage requires the business data to be stored for a long time, this embodiment focuses on the number of mid-term and long-term users, and does not impose specific restrictions on the number of SUs. Specifically, the more LUs and MUs there are, the more stable storage of business data is required; and the higher the frequency of use of business data by users, the stronger the demand for stable storage of business data. Therefore, the degree of stable storage of business data that can be reflected by LU is higher than that that can be reflected by MU. In order to quantify the difference between LU and MU, this embodiment still converts each LU according to the average frequency of use of business data by LU f1, and the average frequency of use of business data by MU f2. MU, For the convenience of distinction and description, in this embodiment, the count value with SU as the counting unit generated when screening the high-utilization service data is referred to as the first count value, and the count value with MU as the counting unit generated when screening the stable storage service data is referred to as the second count value.

[0054] Step 2: Filter the business data that meets the stable storage requirements based on the sum of the second count values ​​of each row. Through step 1, the LU of each row is converted into the second count value of the MU, and the second count value of the MU element itself is 1. Add the second count values ​​of the same row, and the business data corresponding to the row whose sum of the second count values ​​is greater than the set threshold is the business data that meets the stable storage requirements. Preferably, the set threshold is 4.

[0055] When the three conditions of single source, high availability and stable storage are used as the screening conditions for business master data at the same time, the business data that meets all three requirements will eventually be taken as the business master data. Optionally, if there are requirements for data value in certain cases, you can also add the screening condition of high data value. High data value means that the data is indispensable in business applications and has a great impact on business management efficiency, operating costs, driver experience, energy conservation and environmental protection, etc. A data value function g(u) can be constructed to measure the data value of business data u, and ultimately the data that meets the above four requirements will be screened as business master data. The screened business master data can together constitute the infrastructure business master data set. for For example, Including construction delivery data, maintenance construction and delivery data, etc.

[0056] S140: Construct a joint matrix for representing the dual correlation between the business data and the infrastructure data according to the correlation coefficients between each business data and each infrastructure data, and the correlation coefficients between each business data.

[0057] Infrastructure data refers to a series of data categories that highway infrastructure can provide. According to the type of infrastructure, infrastructure data can include routes, structures, traffic safety facilities, management facilities, service facilities and other categories. Each infrastructure data further includes multiple infrastructure data elements. For example, a route includes data elements such as route name, starting pile number, end pile number, route length, etc. Infrastructure data elements are the smallest data units of infrastructure data. In this embodiment, multiple infrastructure data elements are selected from all infrastructure data elements as elements of infrastructure master data.

[0058] It can be seen that infrastructure data is the direct source of infrastructure master data elements. Considering that there is also a data circulation relationship between business data, this step describes the data relationship from two perspectives: the association relationship between business data and infrastructure data, and the association relationship between business data, and simultaneously represents the two relationships through a special matrix form. This embodiment refers to this special matrix form as a joint matrix. In a specific implementation, the construction of the above joint matrix may include the following steps:

[0059] Step 1: Determine the correlation coefficient between each business data and each infrastructure data. Specifically, the infrastructure data set can be represented as Combine each business data u with each infrastructure data The correlation coefficient between u,k . Each a u,k It can be determined through questionnaires or expert scoring, or through interaction data between business systems.

[0060] Optionally, when the determination is made through a questionnaire or expert scoring, for each pair of business data u and infrastructure data k, the correlation between the two data can be expressed as strong and weak, corresponding to the correlation coefficient a u,k =1 and a u,k =2; multiple a can be obtained through multiple questionnaires or multiple expert scoring u,k , take all a u,k The average value was taken as the final correlation coefficient.

[0061] Optionally, when determined by interactive data between business systems, when each business data is determined based on the input and output files of the functional modules in the business system, the correlation coefficient between each business data and each infrastructure data can be determined based on the data interface of the functional modules in each business system and each infrastructure. Specifically, first, the input files and output files of each functional module in each business system can be extracted, and the input files and output files with the current business data as the subject can be determined, and the data in the input files together constitute the input data set F1, and the data in the output files together constitute the output data set F2; at the same time, the input data set I1 and the output data set I2 of each infrastructure can be determined based on the data interface of each infrastructure. Then, for each pair of business data u and infrastructure data k, determine the intersection P1 of the input data set F1 of business data u and the output data set I2 of infrastructure k, and the intersection P2 of the output data set F2 of business data u and the input data set I1 of infrastructure k; both intersections have data as elements, which are called data intersections; take the union of data intersections P1 and P2, and determine the total amount of elements in the union (i.e., the total amount of data). The larger the total amount, the higher the degree of association between business data u and infrastructure data k. After performing the above operations on each pair of business data and infrastructure data, the total amount of data for each pair of business data and infrastructure data can be obtained. These total amounts of data are normalized, and then the normalized values ​​are scaled to the same interval, such as [0,2], so that the correlation coefficient a between each pair of business master data and infrastructure data can be obtained. u,k .

[0062] Step 2: Construct a full matrix with each business data as a row, each infrastructure data as a column, and the correlation coefficient between the business data in the row and the infrastructure data in the column as an element. That is, the element in the i-th row and the j-th column of the matrix is ​​the correlation coefficient between the business data with index i and the infrastructure data with index j. u,k .

[0063] Step 3: Determine the correlation coefficient between each business data. Specifically, the business data u and the business data The correlation coefficient between u,u' , each cu,u' It can also be determined by questionnaires or expert scoring, or by interactive data between business systems. For ease of distinction and description, this embodiment refers to the correlation coefficient between business data and infrastructure data in the above step as the first correlation coefficient, and the correlation coefficient between business data in this step as the second correlation coefficient.

[0064] Optionally, when determined by questionnaire or expert scoring, for each pair of business data u and u', the second correlation relationship between the two data can be expressed as strong, medium, and weak, corresponding to the second correlation coefficient c u,u' =1, c u,u' =2 and c u,u' =3; by multiple decomposition questionnaires or multiple experts scoring multiple c u,u' , take all c u,u' The average value is taken as the final second correlation coefficient.

[0065] Optionally, when the business data is determined by the interactive data between business systems, when each business data is determined according to the input and output files of the functional modules in the business system, the second correlation coefficient between each business data can be determined according to the functional modules in each business system that use each business master data as input or output. Specifically, first, in each business system, a set of functional modules that use each business master data as the subject of the input file and a set of functional modules that use each business master data as the subject of the output file can be located. For the convenience of distinction and description, the above two sets of functional modules are referred to as the first set of functional modules and the second set of functional modules in turn. Then, for each pair of business master data u and u', the intersection Pu1 of the first set of functional modules of business master data u and the second set of functional modules of u' and the intersection Pu2 of the second set of functional modules of business master data u and the first set of functional modules of u' are determined respectively; both of these intersections use functional modules as elements, which are called module intersections; the module intersections Pu1 and Pu2 are taken as a union, and the total amount of elements in the union (i.e., the total amount of modules) is determined. The larger the total amount, the higher the degree of association between business data u and u'. After performing the above operation on each pair of business data, the total amount of the modules of each pair of business data can be obtained. These module total amounts are normalized, and then the normalized values ​​are scaled to the same interval, such as [0,3], so that the second correlation coefficient c between each pair of business data can be obtained. u,u' .

[0066] Step 4: construct a symmetric matrix with each business data as row and column, and the second correlation coefficient between the business data in the row and column as element, that is, the element of the i-th row and j-th column of the matrix is ​​the second correlation coefficient c between the business data with index i and the business data with index j u,u' .

[0067] Step 5: splice and display the lower diagonal matrix of the symmetric matrix on the row index side of the full matrix to obtain a joint matrix for representing the dual correlation between business data and infrastructure data. Figure 3 An example of a joint matrix is ​​shown, in which the rectangular area in the lower right corner of the matrix is ​​the full matrix, the triangular area on the left side of the matrix is ​​the lower diagonal matrix of the symmetric matrix, and the values ​​of each element in the matrix are only for reference and are not fixed values.

[0068] S150. According to the joint matrix and master data screening principle, determine the infrastructure master data elements in units of business master data.

[0069] Based on the above joint matrix, this embodiment applies the master data screening principle to determine the infrastructure master data element set corresponding to each business master data, and takes the union of all infrastructure master data element sets to obtain the final infrastructure master data element set. In a specific implementation, the process may include the following steps:

[0070] Step 1: for each row of business data (including each row of business master data) in the joint matrix, determine at least one infrastructure data whose first correlation coefficient is greater than a certain threshold, and the at least one infrastructure data together constitutes an infrastructure data set associated with the current business master data Optionally, in the above joint matrix, the set of infrastructure data in the same row corresponding to each row of business master data can be recorded as Among them, D u,k is the set of data elements corresponding to the infrastructure data k. For example, the infrastructure data set corresponding to the design information Including route dataset D 2,1 , structure data set D 2,2 , Traffic safety facilities dataset D 2,3 , Management facility dataset D 2,4 , Service Facility Dataset D 2,5 etc., where the route dataset further includes multiple data elements, which can be expressed as D 2,1 ={route name, starting pile number, end pile number, route length, ...}. This step is for the correlation coefficient a u,k Set a threshold, such as 1.5, and use this threshold to calculate the value of each row of business data. To update. Specifically, if a u,k ≥1.5, then keep D u,k unchanged, otherwise D u,k Set it to empty, and then get the new infrastructure data set corresponding to each row of business data in,

[0071]

[0072] Step 2: Perform the following operations for each business master data:

[0073] S2-1, determine other business data that is not business master data and has a second correlation coefficient with the current business master data greater than another threshold. In this step, another threshold is set for the second correlation coefficient, and other business data that is closely related to the business master data and is not business master data is screened according to the threshold. In this embodiment, the other business data is referred to as target business data. Optionally, the other threshold can be 1. For any pair of business master data u and business data If c u,u' ≥1, u' is used as the target business data of business master data u.

[0074] S2-2. Associating the target business data with the infrastructure data set As another infrastructure data collection associated with the current business master data After step 1, the current business master data already has an associated infrastructure data set Due to the strong correlation between the target business data and the current business data, this step collects the infrastructure data associated with the target business data. As another infrastructure data collection associated with the current business master data The formula is as follows:

[0075]

[0076] For the convenience of distinction and description, this embodiment will The infrastructure data in the current business master data is called the first infrastructure data. The infrastructure data in is called the second infrastructure data of the current business master data.

[0077] S2-3, taking a union of the first infrastructure data and the second infrastructure data of the current business master data, and filtering infrastructure master data elements that meet the master data screening principle from the union. Optionally, the union can be expressed as Where m represents c u,u' ≥1 number of target business data, Indicates the second infrastructure data set corresponding to the target business data with index m. Select certain master data screening principles, such as compliance, uniqueness and stability, and screen infrastructure data elements that meet the master data screening principles from the above set. Optionally, the master data screening principle can be represented by a master data screening function f(*), then the master data element M can be represented as:

[0078]

[0079] Among them, uniqueness can be described by data representation consistency and data value consistency, where data representation consistency is represented by the number of data elements with consistent metadata in infrastructure data in different business data. Stability can be described by the number of data updates, where the number of updates is represented by the number of frequently modified data elements. Compliance can be described by data type division, granularity, etc., where data type division is represented by the number of data elements with accurate data type division. For example, if the master data screening function is data representation consistency, then the screening The data elements that are consistent with the metadata of the infrastructure data are used as master data elements.

[0080] By performing the same operation on each business master data, the infrastructure master data element corresponding to each business master data can be obtained. By taking the union of the infrastructure master data elements corresponding to all business master data, the final infrastructure master data element set can be obtained.

[0081] In summary, this embodiment provides a method for determining infrastructure master data elements for the entire life cycle of a highway. According to the data requirements of typical businesses in the entire life cycle of a highway, the association relationship between business data and infrastructure data, as well as the association relationship between business data, reasonably determine the highway infrastructure master data throughout the entire life cycle, and promote the interactive sharing of infrastructure data in different business scenarios. Specifically, the method first constructs a data-process matrix to characterize the data flow of business data in each business scenario based on the source and usage of business data, and selects business master data that meets the source, utilization and storage requirements; then, based on the business master data, the infrastructure data directly associated with the business master data and the infrastructure data indirectly associated with other business data are determined respectively, and the infrastructure data elements corresponding to the current business master data are determined by the two associated infrastructure data. The entire method uses business master data as a bridge, integrates the association relationship between business data and infrastructure data, and the association relationship between business data into the screening of infrastructure master data elements, and can further improve the application value of infrastructure master data elements in business scenarios.

[0082] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 4 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 4The example of connecting through bus is taken in the following.

[0083] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the method for determining the master data elements of infrastructure for the entire life cycle of a highway in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, implements the above-mentioned method for determining the master data elements of infrastructure for the entire life cycle of a highway.

[0084] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0086] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining infrastructure master data elements for the entire life cycle of a highway in any embodiment.

[0087] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0088] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0089] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0090] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining infrastructure master data elements for the entire life cycle of a highway, characterized in that: include: Obtain multiple business scenarios throughout the life cycle of a highway, as well as the infrastructure-related business data that can be produced and required by each business scenario; According to the source and usage of each business data, a data-process matrix is ​​constructed to characterize the data flow of business data in each business scenario; According to the data-process matrix, select various business master data whose data flows meet the requirements of source, utilization and storage; Constructing a joint matrix for representing the dual association relationship between the business data and the infrastructure data according to the first association coefficient between each business data and each infrastructure data, and the second association coefficient between each business data; According to the joint matrix and the master data screening principle, infrastructure master data elements are determined based on business master data, wherein the infrastructure data element is the smallest data unit of infrastructure data.

2. The method according to claim 1, characterized in that The acquisition of multiple business scenarios in the entire life cycle of the highway, as well as the business data related to the infrastructure that can be produced and required by each business scenario, includes: Determine multiple primary business scenarios based on multiple business systems throughout the highway life cycle; Determine the secondary business scenarios included in each primary business scenario based on the functional modules related to infrastructure in each business system; Crawl the infrastructure-related business data required by each secondary business scenario from the input file collection of each functional module; From the output file collection of each functional module, crawl the infrastructure-related business data that can be produced by each secondary business scenario.

3. The method according to claim 1, characterized in that Business scenarios include primary business scenarios and secondary business scenarios; Accordingly, according to the source and usage of each business data, a data-process matrix is ​​constructed to characterize the data flow of business data in each business scenario, including: Build an empty data-process matrix with each business data as row and each secondary business scenario as column; For each row of business data, perform the following operations: S1-1. Mark the secondary business scenarios that can produce current business data as producers; S1-2, marking the secondary business scenario that requires the current business data as a user; S1-3. Re-mark users who belong to the same primary business scenario as the producer as short-term users; S1-4. Users who belong to different primary business scenarios from the producer and whose frequency of using the current business data is less than the set threshold are re-marked as mid-term users; S1-5. Re-mark users who belong to different primary business scenarios from the producer and whose usage frequency of the current business data is greater than or equal to the set threshold as long-term users; S1-6. Assign the final mark of each secondary business scenario to the element of the corresponding column of the current row.

4. The method according to claim 1, characterized in that: The data-process matrix has business data as rows and secondary business scenarios as columns; the elements in the data-process matrix include producers, short-term users, medium-term users and long-term users, which respectively represent the roles of the secondary business scenarios in the columns in the data flow of the business data in the rows; Accordingly, the data flow according to the data-process matrix is ​​screened to select each business master data that meets the source, utilization and storage requirements, including: Filtering business data that meets the single-source requirement according to the number of producer elements in each row of the data-process matrix; According to the time span between the three types of users and producers and the frequency of use of business data, each user element in the data-process matrix is ​​converted into a first count value with short-term users as the counting unit; according to the sum of the first count values ​​of each row, the business data that meets the high utilization requirement is screened; According to the frequency of use of the business data by the mid-term users and the long-term users, each mid-term user and long-term user element in the data-process matrix is ​​converted into a second count value with the mid-term user as the counting unit; and according to the sum of the second count values ​​of each row, the business data that meets the stable storage requirement is screened; Take the business data that meets the three requirements as the master data of each business.

5. The method according to claim 1, characterized in that The constructing of a joint matrix for characterizing the dual association relationship between the business data and the infrastructure data according to the first association coefficient between each business data and each infrastructure data, and the second association coefficient between each business data, comprises: Determine a first correlation coefficient between each business data and each infrastructure data according to the function modules of each business system and the data interface of each infrastructure; A full matrix is ​​constructed with each business data as a row, each infrastructure data as a column, and the first correlation coefficient between the business data in the row and the infrastructure data in the column as an element; Determine a second correlation coefficient between each business data according to a function module in each business system that takes each business data as input or output; A symmetric matrix is ​​constructed by taking each business data as a row and a column respectively and taking the second correlation coefficient between the business data in the row and the column as an element; The lower diagonal matrix of the symmetric matrix is ​​spliced ​​and displayed on the row index side of the full matrix to obtain a joint matrix for representing the dual correlation relationship between business data and infrastructure data.

6. The method according to claim 5, characterized in that Determining the first correlation coefficient between each business data and each infrastructure data according to the functional modules of each business system and the data interface of each infrastructure includes: According to the functional modules of each business system, determine the input files and output files with each business data as the theme; Determine the input data set and output data set of each infrastructure according to the data interface of each infrastructure; Determine a first data intersection of a data set in an input file with each business data as a subject and an output data set of each infrastructure, and a second data intersection of a data set in an output file with each business data as a subject and an input data set of each infrastructure; A union is obtained for the first data intersection and the second data intersection between each business data and each infrastructure, and the total amount of data in the union is converted into a first correlation coefficient between each business data and each infrastructure data.

7. The method according to claim 5, characterized in that Determining the correlation coefficient between each business data according to the functional module with each business data as input or output in each business system includes: In each business system, locate a first set of functional modules that use each business data as input, and a second set of functional modules that use each business data as output; Take the first module intersection of the first function module set of one type of business data and the second module intersection of the second function module set of the other type of business data, and the second module intersection of the second function module set of the one type of business data and the first function module set of the other type of business data; A union is obtained for the intersection of the first module and the intersection of the second module of every two types of business data, and the total amount of modules in the union is converted into a correlation coefficient between every two types of business data.

8. The method according to claim 1, characterized in that The determining of infrastructure master data elements based on the joint matrix and master data screening principles and taking business master data as units includes: For each row of business data in the joint matrix, respectively determine first infrastructure data having a first correlation coefficient greater than a first threshold; For each business master data, perform the following operations respectively: S2-1, determining other business data that has a second correlation coefficient with the current business master data greater than another threshold and is not the business master data; S2-2, determining the first infrastructure data of the other business data as the second infrastructure data associated with the current business master data; S2-3, taking a union of the first infrastructure data and the second infrastructure data of the current business master data, and filtering infrastructure master data elements that meet the master data screening principle from the union; The union of the infrastructure master data elements corresponding to each business master data is taken as the final infrastructure master data element set.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining infrastructure master data elements for the entire life cycle of a highway as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, which, when executed by a processor, implements the method for determining the master data elements of infrastructure for the entire life cycle of a highway as described in any one of claims 1-8.

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