Data relation model dynamic construction method and system for different business scenes of road

Abstract

The invention discloses a data relation model dynamic construction method and system for different road business scenes, and relates to the technical field of data management, and the method comprises the steps: obtaining a road business scene set, and generating an output data format matrix; grouping the business scenes based on the output data items to obtain business scene subgroups; determining a target output data format matrix in the subgroup, and reversely deducing an input data item set and an input data format matrix in combination with the calculation model; searching corresponding data items on a data platform, establishing a data association relation table, performing format conformity judgment, executing data processing when the format conformity is not met, and generating a data acquisition request or a user input request when the format is missing; and constructing a data relation model, extracting data, performing input compliance check, and iteratively updating the matrix, the association table and the relation model according to user evaluation feedback. Through the technical scheme of the invention, the cross-scene data aperture unification and multiplexing are realized, the maintenance cost is reduced, and the access stability and the input data quality are improved.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a method for dynamically constructing data relationship models for different business scenarios of highways, and a system for dynamically constructing data relationship models for different business scenarios of highways. Background Technology

[0002] The highway industry, across its various business areas including construction, maintenance, operation, and road administration, generally faces application needs across multiple business scenarios, such as daily travel route selection, differentiated toll route selection, tourist travel route planning, and emergency guidance. These business scenarios typically require outputting specific data results to upper-level applications, with differentiated requirements for output data items and their formats (such as field types, value ranges, precision, and units of measurement). Simultaneously, each business scenario relies on multi-source data provided by the data platform as input data for its computational models.

[0003] In existing technologies, the mapping relationships between output data items, input data items, and data items on the data platform required by business scenarios are mostly established through manual sorting and pre-fixed configuration. Because different business scenarios have inconsistent format requirements for the same type of data, and business scenarios iterate frequently, the mapping relationships need to be repeatedly maintained, making cross-scenario reuse difficult. When the data format requirements of existing data items on the data platform do not match the input format of the business model, additional manual investigation and processing are often required, easily leading to problems such as inconsistent data definitions, unmet format requirements, and non-compliant model inputs. When the data platform lacks required data items, the lack of a missing item list and a collection / input request mechanism specific to the business model's input format makes it difficult for the business model to operate stably.

[0004] At the same time, existing technologies generally lack a mechanism for continuous iteration of output requirements, relationships, and relationship models based on user feedback, making it difficult to form a data relationship model construction process that can be optimized in a closed loop. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for dynamically constructing data relationship models for different business scenarios in highways. Through a dynamic data relationship model construction mechanism driven by the output requirements of business scenarios, it first generates an output data format matrix and groups business scenarios according to the same output data items. Then, within each subgroup, it uses the highest-requirement function to unify the format standards for different scenarios. Combined with the computational model, it reversely determines the set of input data items and the input data format matrix, reducing manual configuration and repetitive maintenance across scenarios. Furthermore, by establishing an association table between output / input and data platform data items and performing format compliance checks, it processes and completes data for non-compliant items and triggers data collection or user input for missing items, forming a closed-loop data supply system. This improves the consistency and usability of the computational model input. Data is retrieved based on the data relationship model, and input compliance checks are performed to reduce the risk of abnormal data being included in the model. Finally, iterative updates to the matrix, association table, and relationship model are combined with user feedback to improve the continuous adaptability to business changes.

[0006] To achieve the above objectives, this invention provides a method for dynamically constructing data relationship models for different business scenarios in highways, including: Obtain the set of highway business scenarios to be supported, and determine the output data items and corresponding data format requirements for each business scenario in the set of highway business scenarios, and generate the output data format matrix corresponding to each business scenario. Based on the output data items of each business scenario, the highway business scenario set is grouped to obtain business scenario subgroups and the set of the same output data items corresponding to each business scenario subgroup. For the business scenario subgroup, based on the data format requirements of the same set of output data items within the business scenario subgroup, the target output data format matrix of the business scenario subgroup is determined; Based on the target output data format matrix of the business scenario subgroup and the calculation model corresponding to the business scenario subgroup, determine the set of input data items and the input data format matrix to meet the output requirements of the calculation model; For the set of input data items, the data platform data items corresponding to each input data item are searched in the data platform, and a data association table is established. The data association table includes at least: output data items and output data formats, input data items and input data formats, data platform data item identifiers and data platform data format requirements. Based on the data association table, the data format requirements of the data platform data items are judged to be compatible with the input data format matrix; When the data format requirements of the conformity determination data platform data item do not meet the input data format matrix, data processing is performed on the corresponding data platform data item; when the data platform is missing a data platform data item corresponding to the input data item, a data collection request or user input request is generated. Based on the data association table and the compliance determination result, a data relationship model corresponding to the business scenario subgroup is constructed, and input data for the calculation model is obtained based on the data relationship model; compliance checks are performed on the input data to determine whether the input data meets the input data format matrix; The output data items and corresponding data format requirements are updated based on user feedback, and the output data format matrix, the data association table, and the data relationship model are iteratively updated accordingly.

[0007] In the above technical solution, preferably, the highway business scenario set includes at least business scenarios in the fields of highway construction, maintenance, operation and road administration. For the aforementioned business scenario, application scenario requirement data items were generated through a questionnaire survey of highway management personnel and travelers; The output data format matrix includes at least the output data items, field descriptions, data types, data value ranges, data precision, and units of measurement.

[0008] In the above technical solution, preferably, the specific process of grouping the highway business scenario set includes: Compare the output data items of the business scenario to be grouped with the set of identical output data items of the established business scenario subgroups. When the number of identical output data items is 0, a new business scenario subgroup is created. When the number of identical output data items is not less than a preset threshold, the business scenario to be grouped is added to the corresponding business scenario subgroup, and the set of identical output data items in the corresponding business scenario subgroup is updated.

[0009] In the above technical solution, preferably, the specific process of determining the target output data format matrix includes: For the set of identical output data items, the data format requirements corresponding to the identical output data items are compared, and the target output data format matrix is ​​calculated based on the highest requirement function. The highest requirement function for data precision outputs a data precision requirement with a smaller precision value.

[0010] In the above technical solution, preferably, the specific process of determining the set of input data items and the input data format matrix based on the target output data format matrix and the calculation model includes: Based on a preset mapping function, the computational model and the target output data format matrix are mapped to a set of input data items and an input data format matrix that meet the output requirements of the computational model.

[0011] In the above technical solution, preferably, the data association table further includes a supporting application identifier, and the data platform data item identifier is represented in the manner of "topic database identifier - data item identifier".

[0012] In the above technical solution, preferably, the data processing is used to supplement data platform data items that do not meet the input data format matrix to meet the input data format matrix; and the list of missing data items includes at least the identifier of the missing input data item and its corresponding input data format requirements.

[0013] In the above technical solution, preferably, for different output data items in the application scenario, the established data relationship model is searched to determine the corresponding input data item and input data format; when the search result is empty, the data association table is updated and the data relationship model corresponding to the different output data item is re-established.

[0014] In the above technical solution, preferably, when the business scenario subgroup includes daily travel route selection, differentiated toll route selection, tourism travel route planning, and emergency route planning guidance business scenarios, the set of input data items includes at least service area queuing information, toll station queuing information, maintenance plans and construction information, traffic operation status information, traffic congestion warning information, meteorological disaster warning information, traffic incident road control measures, emergency command and control information, and historical ETC passage records.

[0015] This invention also proposes a dynamic data relationship model construction system for different business scenarios of highways, used to implement the dynamic data relationship model construction method for different business scenarios of highways disclosed in any of the above technical solutions, including: The business scenario management module is used to obtain a set of highway business scenarios to be supported, and to determine the output data items and corresponding data format requirements for each business scenario in the set of highway business scenarios, and generate an output data format matrix for each business scenario. The business scenario grouping module is used to group the highway business scenario set based on the output data items of each business scenario, and obtain a business scenario subgroup and a set of the same output data items corresponding to the business scenario subgroup. The target format determination module is used to determine the target output data format matrix of the business scenario subgroup based on the data format requirements of the same set of output data items within the business scenario subgroup. The input requirement determination module is used to determine the set of input data items and the input data format matrix to meet the output requirements of the calculation model based on the target output data format matrix of the business scenario subgroup and the calculation model corresponding to the business scenario subgroup. The data association construction module is used to find the data platform data item corresponding to each input data item in the data platform for the set of input data items, and to establish a data association table. The data association table includes at least: output data items and output data format, input data items and input data format, data platform data item identifier and data platform data format requirements. The conformity determination module is used to determine the conformity of the data format requirements of the data platform data items with the input data format matrix based on the data association table. The data supplementation and acquisition module is used to perform data processing on the corresponding data platform data item when the data format requirements of the conformity determination characterization data platform data item do not meet the input data format matrix; and to generate a data acquisition request or user input request when the data platform is missing a data platform data item corresponding to the input data item. The model building and checking module is used to build a data relationship model corresponding to the business scenario subgroup based on the data relationship table and the compliance judgment result, and to obtain input data for the calculation model based on the data relationship model; and to perform a compliance check on the input data to determine whether the input data meets the input data format matrix. The feedback iteration update module is used to update the output data items and corresponding data format requirements based on user feedback, and to iteratively update the output data format matrix, the data association table and the data relationship model accordingly.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By constructing an output data format matrix based on the output requirements of business scenarios and grouping business scenarios based on output data items, reusable business scenario subgroups can be formed in the case of multiple business scenarios running in parallel, reducing the cost of cross-scenario repetitive sorting and configuration maintenance, and improving the scale efficiency of data relationship construction.

[0017] (2) By introducing the highest requirement function in the business scenario subgroup to determine the target output data format matrix, and combining the calculation model to back-infer the set of input data items and the input data format matrix, the format of the same type of output data items in different scenarios can be unified, ensuring the consistency between the input and output requirements of the calculation model, and reducing the risk of calculation deviation and incomparability of results caused by differences in data format.

[0018] (3) By establishing a data association table that includes output / input / data platform data items and their format requirements, and performing compliance judgment on the data platform data item format and input data format matrix, the format compliance verification can be completed before the data enters the calculation model; when it is not satisfied, data processing is performed to fill in the gaps; when the data platform is missing, a data collection request or user input request is generated, realizing the closed-loop supplementation of the data supply side and improving the availability and stability of the model.

[0019] (4) By constructing a data relationship model based on the data association table and the compliance judgment results and obtaining the model input data, and performing compliance checks on the input data, the probability of abnormal data entering the model can be further reduced, the quality of input data and the reliability of the calculation process can be improved, and manual investigation and rework can be reduced.

[0020] (5) By introducing user evaluation feedback to update the output data items and data format requirements, and iteratively outputting the data format matrix, data association table and data relationship model accordingly, the continuous evolution and adaptive optimization of the data relationship model can be achieved, which improves the system's response speed and long-term adaptability to changes in business scenarios. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a method for dynamically constructing data relationship models for different business scenarios in highways, as disclosed in one embodiment of the present invention. Figure 2 This is a schematic diagram of a data relationship architecture for different business scenarios disclosed in one embodiment of the present invention; Figure 3 This is a schematic diagram of a data association model disclosed in one embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The present invention will now be described in further detail with reference to the accompanying drawings: like Figure 1 As shown, a method for dynamically constructing a data relationship model for different business scenarios of highways, according to the present invention, includes: Around the business applications of the entire life cycle of highway construction, maintenance, operation, and road administration, a set of highway business scenarios to be supported is obtained. For each business scenario in the set of highway business scenarios, the output data items of the business scenario are extracted and determined, and data format requirements are configured for each output data item. The output data format matrix corresponding to each business scenario is generated to uniformly describe "what data the business needs to output and what format requirements the data should meet".

[0024] After forming output data format matrices for multiple business scenarios, the highway business scenario set is grouped based on the output data items of each business scenario, resulting in business scenario subgroups and a set of identical output data items corresponding to each subgroup. For each business scenario subgroup, the data format requirements for the same output data items in each business scenario within the subgroup are compared around the set of identical output data items. This comparison yields the target output data format matrix for the subgroup, which is used to establish a consistent output format standard within the subgroup and avoid discrepancies in the application of the same output data item across different scenarios.

[0025] Based on the target output data format matrix of the business scenario subgroup and the corresponding computing model of the business scenario subgroup, the set of input data items and the input data format matrix used to meet the output requirements of the computing model are determined in reverse, with the target output data format matrix as a constraint, so that the input side and the output side form a traceable mapping relationship in terms of data items and format requirements.

[0026] On the data supply side, for each set of input data items, the corresponding data platform data item is located in the data platform, and a data relationship table is established. This relationship table records the output data items and their formats, the input data items and their formats, the data platform data item identifiers, and the data platform data format requirements, thus correlating business outputs with model inputs and platform data.

[0027] Based on this data association table, a conformity judgment is performed on the data format requirements of the data platform data items and the input data format matrix. When the judgment is not satisfied, data processing is performed on the corresponding data platform data items to satisfy the input format matrix. When the data platform is missing a corresponding data item, a data collection request or user input request is triggered to supplement the input data required by the model.

[0028] After completing compliance verification and completion, a data relationship model corresponding to the business scenario subgroup is constructed based on the data relationship table and the compliance judgment results. Input data for calculating the model is obtained based on the data relationship model. Compliance checks are performed on the input data to determine whether the input data meets the input data format matrix, forming a closed-loop consistency control of "format requirements - platform data - input data".

[0029] Finally, based on user feedback, the output data items and corresponding data format requirements are updated, and the output data format matrix, data relationship table, and data relationship model are iteratively updated accordingly to achieve continuous adaptation to business changes.

[0030] In this implementation, the technical logic of output demand-driven, input reverse-engineering, platform verification and completion, model data compliance and feedback iteration reduces the problems of inconsistent standards and unstable data acquisition caused by data silos, and improves the efficiency of data integration across business scenarios and the input quality of computing models.

[0031] In the above implementation, preferably, the source and structure of the business scenarios and the output data format matrix are subject to fixed constraints. Specifically, the highway business scenario set includes at least business scenarios in the fields of highway construction, maintenance, operation, and road administration, to ensure that the scenario set covers the needs of highway lifecycle management.

[0032] To address specific business scenarios, a questionnaire survey of highway management personnel and travelers was conducted to generate application scenario requirement data items. This ensures that the determination of output data items stems from actual business function outputs and management decision-making needs, rather than being limited by existing fields on the data platform.

[0033] In defining the structure of the output data format matrix, the output data items and their format requirements are expressed in a matrix manner. The matrix includes at least six elements: output data items, field descriptions, data types, data value ranges, data precision, and units of measurement. This ensures that the output requirements of different business scenarios maintain a consistent expression in terms of field semantics, type boundaries, numerical constraints, precision, and units, providing a unified constraint basis for subsequent scenario grouping, target output format aggregation, input requirement deduction, and platform compliance verification.

[0034] In this implementation, the six elements of the demand data items and output format matrix are solidified through questionnaire surveys, which realizes the quantification and comparability of output demands and reduces semantic ambiguity and caliber drift of different departments and systems for the same data item.

[0035] In the above embodiments, preferably, the specific process of grouping the highway business scenario set includes: When dealing with grouped business scenarios, first compare their output data items with the set of identical output data items of the established business scenario subgroups: when the number of identical output data items is 0, initialize the business scenario as a new business scenario subgroup and simultaneously initialize the set of identical output data items of the subgroup, so that each business scenario has a clear affiliation during the grouping stage.

[0036] When the number of identical output data items between the business scenario to be grouped and a certain business scenario subgroup is not less than a preset threshold, the business scenario to be grouped is added to that business scenario subgroup, and the set of identical output data items in that business scenario subgroup is updated. The update action is used to keep the set of shared output data items within the subgroup closed, avoiding the use of the old set after a new scenario is added to the subgroup, which would lead to missing or incorrect items in the subsequent calculation of the target output format matrix.

[0037] In this implementation, threshold grouping and dynamic updating of the same output data item set improve grouping stability and consistency, and reduce the output format aggregation deviation caused by unclear subgroup boundaries.

[0038] In the above embodiments, preferably, determining the target output data format matrix is ​​used to refine the method of determining the target output data format matrix, and the specific process includes: For a set of identical output data items, the data format requirements of each identical output data item within the subgroup are compared one by one. The highest requirement function is used to summarize various format elements to form a target output data format matrix, so that the target matrix forms a unified constraint on the same output data item within the subgroup.

[0039] In terms of data precision, the highest requirement is that the function output has a smaller precision value, which makes the target output format more strictly defined in terms of precision constraints. This results in a unified and reusable output precision standard within the subgroup, avoiding inconsistencies in the model output precision after aggregation due to the use of coarse precision in a certain scenario.

[0040] In this implementation, by summarizing the highest required function and taking the smaller value of precision, the upper bound of the format of the same output data item within the subgroup is unified, reducing the risk of format conflicts and precision inconsistencies when summarizing across scenarios.

[0041] In the above implementation, preferably, the set of input data items and the input data format matrix are determined based on the target output data format matrix and the calculation model, which are used to infer the input requirements from the target output. The specific process includes: Based on a pre-defined mapping function, the computational model and the target output data format matrix are mapped to a set of input data items and an input data format matrix that meet the output requirements of the computational model. The mapping function, constrained by the output requirements of the computational model and bounded by the field semantics and format requirements of the target output format matrix, determines the input data items that the model must acquire and the format requirements that each input data item must meet, thus achieving traceable back-input from output. This mechanism ensures that the generation of the input data item set and the input data format matrix does not rely on manual item-by-item compilation, but rather that the mapping function forms a stable and reusable input requirement result, creating a consistent data item link with the subsequent construction of the data association table.

[0042] In this implementation, the input requirements of the computing model are structured and output through a mapping function, which reduces the cost of manual configuration and improves the consistency of input requirements under the same computing model for different business scenarios.

[0043] In the above embodiments, preferably, the data association table further includes a supporting application identifier to distinguish the calling relationship and responsibility boundary of the same data item under different supporting applications, so as to avoid the same data association link from being covered and confused when multiple applications are running in parallel.

[0044] Meanwhile, the data platform data item identifiers are represented in the form of topic database identifier - data item identifier, so that the data source and field location on the data platform side have clear two-level positioning information, which makes it easy to quickly trace back to the specific topic database and field object when determining compliance, data processing and filling missing items, thereby improving the efficiency of related table maintenance and problem location.

[0045] In this implementation, by supporting two levels of identification—application identifier and thematic database—the maintainability and traceability of the data association table are enhanced, and field ambiguity and location costs are reduced when retrieving data across thematic databases.

[0046] In the above implementation, preferably, the output objects of data processing and missing data completion are explicitly constrained. Specifically, data processing is used to supplement data platform data items that do not meet the input data format matrix to meet the input data format matrix, so that the data obtained on the input side is consistent with the input requirements of the calculation model in terms of format requirements, thereby reducing the failure of input model or calculation deviation caused by inconsistent platform field formats.

[0047] When a data platform is missing a data item corresponding to an input data item, a list of missing data items is generated. This list of missing data items records at least the identifier of the missing input data item and its corresponding input data format requirements. This ensures that subsequent data collection requests or user input requests have clear data item boundaries and format constraints, avoiding repeated rework due to the input format matrix not being met even after supplementary data collection.

[0048] In this implementation, by completing data through data processing and outputting a structured list of missing data items, an executable completion loop is formed, which improves the response efficiency of the data supply side to the model input requirements.

[0049] In the above implementation, preferably, a reuse mechanism is established for different output data items in the application scenario. For different output data items in the application scenario, the established data relationship model is first searched to determine the corresponding input data items and input data format. Thus, when the existing model link can be reused, the input requirements and data retrieval relationship can be directly reused, reducing repeated modeling and repeated verification.

[0050] When the search result is empty, update the data association table and rebuild the data relationship model corresponding to the different output data item. This ensures that the different output data items are also included in the unified input format matrix constraints, platform compliance judgment, data processing or supplementary collection logic, and that the newly built model link is consistent with the existing link in terms of management.

[0051] In this implementation, by first finding reused results and then rebuilding empty results, the cost of repeated construction is reduced while ensuring closed-loop consistency, and the speed of launching new business output items is accelerated.

[0052] In the above implementation, preferably, the set of input data items for specific business scenario subgroups is clearly defined. When the business scenario subgroup includes daily travel route selection, differentiated toll route selection, tourism travel route planning, and emergency route planning guidance business scenarios, the set of input data items is configured to at least include service area queuing information, toll station queuing information, maintenance plans and construction information, traffic operation status information, traffic congestion warning information, meteorological disaster warning information, traffic incident road control measures, emergency command and control information, and historical ETC passage records, based on the input dependency of the travel route planning calculation model.

[0053] The aforementioned set of input data items is used to support the route planning calculation model in comprehensively constraining road network traffic status, toll collection and queuing load, construction impact, disaster and event control, emergency dispatch constraints, and historical traffic behavior. This enables the model to form a consistent input base under different travel-related business scenarios, facilitating the reuse of data association tables and data relationship models within the same business scenario subgroup.

[0054] In this implementation, by constraining the minimum set of input data items for the travel-related business scenario subgroups, the model input boundary is stabilized, thereby improving the data reusability and result comparability between different travel scenarios.

[0055] This invention also proposes a dynamic data relationship model construction system for different business scenarios of highways, used to implement the dynamic data relationship model construction method for different business scenarios of highways disclosed in any of the above embodiments, including: The business scenario management module is used to obtain the set of highway business scenarios to be supported, and for each business scenario in the set of highway business scenarios, determine the output data items and corresponding data format requirements of the business scenario, and generate the output data format matrix corresponding to each business scenario. The business scenario grouping module is used to group the highway business scenario set based on the output data items of each business scenario, and obtain the business scenario subgroup and the set of the same output data items corresponding to the business scenario subgroup. The target format determination module is used to determine the target output data format matrix of a business scenario subgroup based on the data format requirements of the same set of output data items within the business scenario subgroup. The input requirement determination module is used to determine the set of input data items and the input data format matrix to meet the output requirements of the calculation model based on the target output data format matrix of the business scenario subgroup and the calculation model corresponding to the business scenario subgroup. The data association construction module is used to find the corresponding data platform data items in the data platform for each input data item set, and to establish a data association table. The data association table includes at least: output data items and output data format, input data items and input data format, data platform data item identifier and data platform data format requirements. The compliance determination module is used to determine the compliance of the data format requirements of the data items on the data platform with the input data format matrix based on the data association table. The data supplementation and acquisition module is used to perform data processing on the corresponding data platform data items when the data format requirements of the data platform data items in the compliance judgment characterization data items do not meet the input data format matrix; and to generate a data acquisition request or user input request when the data platform is missing a data platform data item corresponding to the input data item. The model building and checking module is used to build data relationship models corresponding to business scenario subgroups based on data relationship tables and compliance judgment results, and to obtain input data for calculating the model based on the data relationship models; it performs compliance checks on the input data to determine whether the input data meets the input data format matrix. The feedback iteration update module is used to update the output data items and corresponding data format requirements based on user feedback, and iteratively update the output data format matrix, data association table and data relationship model accordingly.

[0056] In this implementation, by creating a closed loop of data flow between the modules, the system achieves unified format and standards across business scenarios, compliance verification and completion of data platform fields, and continuous iterative updates to the data relationship model, thereby improving the stability and maintainability of computing applications in highway business scenarios.

[0057] The dynamic data relationship model construction system for different business scenarios of highways disclosed in the above embodiments has the same functions as the steps of the dynamic data relationship model construction method for different business scenarios of highways disclosed in the above embodiments. In the implementation process, refer to the above embodiments for operation, and will not be repeated here.

[0058] Specifically, such as Figure 2As shown, the dynamic data relationship model construction system for different highway business scenarios disclosed in the above implementation method forms a highway business scenario set during the implementation process, targeting travel needs in different fields such as highway construction, maintenance, operation, and road administration. This set includes... N Each business scenario can be represented as For business scenarios Data items on application scenario requirements can be generated through questionnaires and surveys of highway management personnel and travelers. R i Taking some business scenarios in the travel service industry as examples, a data relationship model is constructed. This model outlines the corresponding output data requirements for daily travel route selection, differentiated pricing route selection, tourism travel route planning, off-peak guidance services for scenic spots, emergency route planning and guidance, and emergency rescue services. As shown in the table below.

[0059] Data items based on application scenario requirements R i Including research needs, etc., to form an output data format matrix. That is, data requirement items corresponding k Data format requirements include output data items, field descriptions, data types, data value ranges, data precision, and units of measurement.

[0060] Based on daily travel route selection R Taking 1 as an example, it contains 7 data items. The format requirements for each data item include six aspects: output data item, field description, data type, data value range, data precision, and unit of measurement. Therefore, the corresponding data format matrix... As shown in the table below.

[0061] Analyze the common items in the output data requirements of various business scenarios and summarize them into business scenario subgroups. and the corresponding identical output data items and data format matrix The specific process includes: (1) Initialization of business scenario subgroups. Compare the data items required by the application scenario. R j and R i If the number of identical data items is 0, then they are set as business scenario subgroups respectively. and ,Right now .

[0062] (2) For business scenario subgroups Application scenarios included i When business scenarios j Output data R j and R i The number of identical data items is not less than k One, then business scenario j Add to business scenario subgroup The set of identical output data items included in the business scenario output data can be represented as follows: As shown below.

[0063] For the same set of output data items Compare the numbers of the same data items Corresponding data format matrix Regarding the first k Data format requirements necessitate the use of the most demanding functions. f (...) Calculate the output data format matrix , can be represented as follows.

[0064] Here, the highest required function refers to the highest format requirement for all data items. For example, if the precision requirements for different data items are 1 and 0.1 respectively, then the highest required function... f (...) The output result is 0.1.

[0065] During initialization, make the scene R 1. Included in the business scenario subgroup In, that is Analysis revealed that the scene R 1, R 2, R 3, R 5 contains 6 identical data items. Therefore, the business scenario subgroup can be updated to... The corresponding set of identical output data items can be represented as ={Time, Origin, Destination, Waypoints, Total Distance, Average Travel Time}.

[0066] For business scenario subgroups Same output data items and data format matrix To meet the output requirements of business scenarios, the computational model needs input data items. and data format matrix .Right now ,function It can be set according to the specific system calculation model.

[0067] For business scenario subgroups Corresponding input data items and data format matrix Establish a data platform A data relationship model between the input data and the data to be input. (For each input data item...) It contains For each data item, the specific steps for establishing the relational model are as follows: (1) Let Search data platform G i The corresponding data item in the middle.

[0068] (2) If data platform G i This data item is included, thus forming a data relationship table of "supporting applications - output data and data format - input data and data format - data platform data items and data format requirements".

[0069] For business scenario subgroups This includes daily travel route selection, differentiated pricing route selection, tourism travel route planning, and emergency route planning guidance, analyzing the output data items of the travel route planning model. Data items need to be entered. This can be represented as {service area queuing information, toll station queuing information, maintenance plans and construction information, traffic operation status information, traffic congestion warning information, meteorological disaster warning information, traffic incident road control measures, emergency command and control information, and historical ETC passage records}.

[0070] Based on existing data on the data platform, establish subgroups for specific business scenarios. The data is linked to a table. Data items on the data platform are represented by "Special Topic Database - Data Item". See below for details: To establish a data association model for identical output data items in an application scenario, a data association model can be formed, such as... Figure 3 As shown.

[0071] (3) Compare the data format requirements in the data platform to see if the input data and data format requirements are met. If the requirements are not met, supplement them through data processing.

[0072] (4) If data platform G i If the data item is not included, a list needs to be generated and the data collected again, or the user needs to input it.

[0073] For dissimilar output data items in different application scenarios, an existing data relationship model can be consulted or a new one can be created. Based on the established data relationship model, the input data undergoes a compliance check; for identical data items, the input data format matrix must be satisfied. This ensures the data meets requirements. Based on user feedback, the output functions and data requirements are updated, leading to iterative updates to data format requirements and dynamic updates to the data relationship model.

[0074] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamically constructing data relationship models for different business scenarios in highways, characterized in that, include: Obtain the set of highway business scenarios to be supported, and determine the output data items and corresponding data format requirements for each business scenario in the set of highway business scenarios, and generate the output data format matrix corresponding to each business scenario. Based on the output data items of each business scenario, the highway business scenario set is grouped to obtain business scenario subgroups and the set of the same output data items corresponding to each business scenario subgroup. For the business scenario subgroup, based on the data format requirements of the same set of output data items within the business scenario subgroup, the target output data format matrix of the business scenario subgroup is determined; Based on the target output data format matrix of the business scenario subgroup and the calculation model corresponding to the business scenario subgroup, determine the set of input data items and the input data format matrix to meet the output requirements of the calculation model; For the set of input data items, the data platform data items corresponding to each input data item are searched in the data platform, and a data association table is established. The data association table includes at least: output data items and output data formats, input data items and input data formats, data platform data item identifiers and data platform data format requirements. Based on the data association table, the data format requirements of the data platform data items are judged to be compatible with the input data format matrix; When the data format requirements of the conformity determination data platform data item do not meet the input data format matrix, data processing is performed on the corresponding data platform data item; when the data platform is missing a data platform data item corresponding to the input data item, a data collection request or user input request is generated. Based on the data association table and the compliance determination result, a data relationship model corresponding to the business scenario subgroup is constructed, and input data for the calculation model is obtained based on the data relationship model; compliance checks are performed on the input data to determine whether the input data meets the input data format matrix; The output data items and corresponding data format requirements are updated based on user feedback, and the output data format matrix, the data association table, and the data relationship model are iteratively updated accordingly.

2. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The highway business scenario set includes at least business scenarios in the fields of highway construction, maintenance, operation, and road administration. For the aforementioned business scenario, application scenario requirement data items were generated through a questionnaire survey of highway management personnel and travelers; The output data format matrix includes at least the output data items, field descriptions, data types, data value ranges, data precision, and units of measurement.

3. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The specific process of grouping the aforementioned highway business scenario set includes: Compare the output data items of the business scenario to be grouped with the set of identical output data items of the established business scenario subgroups. When the number of identical output data items is 0, a new business scenario subgroup is created. When the number of identical output data items is not less than a preset threshold, the business scenario to be grouped is added to the corresponding business scenario subgroup, and the set of identical output data items in the corresponding business scenario subgroup is updated.

4. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The specific process of determining the target output data format matrix includes: For the set of identical output data items, the data format requirements corresponding to the identical output data items are compared, and the target output data format matrix is ​​calculated based on the highest requirement function. The highest requirement function for data precision outputs a data precision requirement with a smaller precision value.

5. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The specific process of determining the set of input data items and the input data format matrix based on the target output data format matrix and the computation model includes: Based on a preset mapping function, the computational model and the target output data format matrix are mapped to a set of input data items and an input data format matrix that meet the output requirements of the computational model.

6. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The data association table further includes supporting application identifiers, and the data platform data item identifiers are represented in the format of "topic database identifier - data item identifier".

7. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, The data processing is used to supplement data platform data items that do not meet the input data format matrix to meet the input data format matrix; and the list of missing data items includes at least the identifier of the missing input data item and its corresponding input data format requirement.

8. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, For different output data items in the application scenario, the established data relationship model is searched to determine the corresponding input data item and input data format; when the search result is empty, the data relationship table is updated and the data relationship model corresponding to the different output data item is re-established.

9. The method for dynamically constructing data relationship models for different business scenarios of highways according to claim 1, characterized in that, When the business scenario subgroup includes daily travel route selection, differentiated toll route selection, tourism travel route planning, and emergency route planning guidance business scenarios, the set of input data items shall at least include service area queuing information, toll station queuing information, maintenance plans and construction information, traffic operation status information, traffic congestion warning information, meteorological disaster warning information, traffic incident road control measures, emergency command and control information, and historical ETC passage records.

10. A dynamic data relationship model construction system for different business scenarios in highways, characterized in that, The method for dynamically constructing data relationship models for different business scenarios of highways as described in any one of claims 1 to 9 includes: The business scenario management module is used to obtain a set of highway business scenarios to be supported, and to determine the output data items and corresponding data format requirements for each business scenario in the set of highway business scenarios, and generate an output data format matrix for each business scenario. The business scenario grouping module is used to group the highway business scenario set based on the output data items of each business scenario, and obtain a business scenario subgroup and a set of the same output data items corresponding to the business scenario subgroup. The target format determination module is used to determine the target output data format matrix of the business scenario subgroup based on the data format requirements of the same set of output data items within the business scenario subgroup. The input requirement determination module is used to determine the set of input data items and the input data format matrix to meet the output requirements of the calculation model based on the target output data format matrix of the business scenario subgroup and the calculation model corresponding to the business scenario subgroup. The data association construction module is used to find the data platform data item corresponding to each input data item in the data platform for the set of input data items, and to establish a data association table. The data association table includes at least: output data items and output data format, input data items and input data format, data platform data item identifier and data platform data format requirements. The conformity determination module is used to determine the conformity of the data format requirements of the data platform data items with the input data format matrix based on the data association table. The data supplementation and acquisition module is used to perform data processing on the corresponding data platform data item when the data format requirements of the conformity determination characterization data platform data item do not meet the input data format matrix; and to generate a data acquisition request or user input request when the data platform is missing a data platform data item corresponding to the input data item. The model building and checking module is used to build a data relationship model corresponding to the business scenario subgroup based on the data relationship table and the compliance judgment result, and to obtain input data for the calculation model based on the data relationship model; and to perform a compliance check on the input data to determine whether the input data meets the input data format matrix. The feedback iteration update module is used to update the output data items and corresponding data format requirements based on user feedback, and to iteratively update the output data format matrix, the data association table and the data relationship model accordingly.

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