CN117035557A - Highway infrastructure digital degree evaluation method for business scene - Google Patents
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Abstract
The embodiment of the invention discloses a highway infrastructure digital degree evaluation method aiming at a business scene, which comprises the following steps: acquiring a standard service scene set related to highway infrastructure and an actual service scene set to be evaluated; analyzing a data interaction model of the standard service scene set; screening a minimum data set taking each actual service scene as a starting point service scene and/or an end point service scene from the data interaction model to obtain a data interaction sub-model of the actual service scene set; calculating the score of each evaluation index based on the data interaction sub-model; constructing a judgment matrix by taking each evaluation index as a dimension, and executing a analytic hierarchy process based on the judgment matrix to determine the weight of each evaluation index; and carrying out weighted average on the scores and the weights of all the evaluation indexes to obtain the digital degree evaluation value of the road infrastructure under the actual service scene set.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent traffic, in particular to a highway infrastructure digital degree evaluation method aiming at a business scene.
Background
With the rapid development of road traffic mileage, traffic infrastructure stock is continuously rising, and the importance of digital development is increasingly highlighted. The highway infrastructure data is taken as an important foundation and support of highway full life cycle business, is the foundation of highway industry digitization, and the completeness of resources directly influences the digitization degree of the highway industry.
At present, a highway infrastructure digitization degree evaluation method which runs through a full life cycle is lacking, and the digitization effect of the highway infrastructure cannot be predicted according to the actually required business scene. For example, patent CA116341957 discloses a highway infrastructure digitization rate measurement method, CN116089556a discloses a digitization and standardization platform for highway infrastructure and a method thereof, which do not consider the characteristics of the data resources in practical application, and limit the application and perfection of the data resources in practical scenes.
Disclosure of Invention
The embodiment of the invention provides a highway infrastructure digital degree evaluation method aiming at a service scene, which combines the characteristics of data resources in practical application and accurately evaluates the highway infrastructure digital degree based on the service scene and data interaction.
In a first aspect, an embodiment of the present invention provides a method for evaluating a digitalized degree of a highway infrastructure for a service scenario, including:
s110, acquiring a standard service scene set related to highway infrastructure and an actual service scene set to be evaluated;
s120, analyzing a data interaction model of the standard service scene set, wherein the data interaction model comprises minimum data sets interacted among service scenes and a starting point service scene and an ending point service scene of the minimum data sets;
s130, screening minimum data sets taking each actual service scene as a starting point service scene and/or an end point service scene from the data interaction model, and forming a data interaction sub-model of the actual service scene set by the screened minimum data sets, the starting point service scene and the end point service scene;
s140, calculating the scores of all evaluation indexes based on the data interaction sub-model; constructing a judgment matrix by taking each evaluation index as a dimension, and executing a analytic hierarchy process based on the judgment matrix to determine the weight of each evaluation index;
and S150, carrying out weighted average on the scores and the weights of all evaluation indexes to obtain the digital degree evaluation value of the road infrastructure under the actual service scene set.
In a second aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the highway infrastructure digitizing level assessment method for business scenarios of any of the embodiments.
In a third aspect, an embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, where the program when executed by a processor implements the highway infrastructure digitizing degree assessment method for a traffic scenario according to any embodiment.
The embodiment of the invention provides a highway infrastructure digital degree assessment method aiming at a service scene, which takes the service scene and data interaction as constraint conditions and realizes the accurate assessment of the highway infrastructure full life cycle digital degree. Firstly, determining a general standard service scene set in the field of highway infrastructure according to related standards; determining a universal service scene range and a universal data range related to data interaction by analyzing a data interaction model of a standard service scene; and selecting a data range related to the actual service scene to be evaluated from the universal range, and calculating the scores of various evaluation indexes according to the data proportion of various attributes in the data range. Then, a judgment matrix is constructed by taking each evaluation index as a dimension, and the weights of each evaluation index are determined by executing a analytic hierarchy process. The index score and the weight determined by the method can highlight the importance of the service scene and the data interaction in the data evaluation, and reflect the application effect of the data resource based on the data interaction under the specific service scene set. Finally, through the digital degree evaluation value obtained by the score and weight calculation of each index, supporting the data penetration of the infrastructure data in different business scenes, guiding the data base establishment in the basic set donor data identification and digital transformation, and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for evaluating the digital degree of a highway infrastructure for a business scenario according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data interaction model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the invention, are within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Fig. 1 is a flowchart of a method for evaluating the digitalized degree of a highway infrastructure for a service scene according to an embodiment of the present invention. The method is suitable for predicting the digital effect of the highway infrastructure in a specific business scene(s), and is executed by the electronic equipment. As shown in fig. 1, the method specifically includes:
s110, acquiring a standard service scene set related to the highway infrastructure and an actual service scene set to be evaluated.
The standard service scene refers to a general service scene of the whole life cycle of the highway, covers the main stage of the whole life cycle of the highway, and has a certain representativeness. The actual service scene refers to a specific service scene to be evaluated, and is a specific background condition for evaluating the digital degree in the embodiment.
In a specific embodiment, the general infrastructure and each general service scenario of the highway full life cycle can be determined according to the relevant standard; and screening all service scenes related to the universal infrastructure from all the universal service scenes to jointly form a standard service scene set. By way of example, common infrastructure (or understood to be typical infrastructure) includes routes, roadbeds, roadways, bridges, tunnels, culverts, route crossings, highway entrances and exits, traffic engineering, and along-line facilities, and the like. The classifications for each stage are shown in Table 1 below:
the management facilities such as the monitoring facilities, the communication facilities, the charging facilities and the like can be further refined, for example, the monitoring facilities can be refined to seven-level division, and the monitoring facilities comprise monitoring outfield equipment along the line, traffic sensing equipment, traffic running state sensing equipment and video monitoring equipment.
General business scenarios (or understood to be typical business scenarios) include highway construction, administrative, highway maintenance, highway operation, third party interactions, and the like. Wherein the general traffic scenario classification associated with the highway infrastructure is as shown in table 2:
in another embodiment, the actual infrastructure of the highway to be evaluated and the actual service scene related to the implementation infrastructure may be obtained according to the actual application, and each actual service scene forms an actual service scene set. It should be noted that the actual infrastructure and the general infrastructure may be the same, different, or partially the same; similarly, the actual service scenario and the general infrastructure may be the same, different, or partially the same; the actual service scene can be one or a plurality of, and the actual service scene is used for limiting the evaluation range and can be flexibly determined according to the needs in the actual application.
S120, analyzing a data interaction model of the standard service scene set, wherein the data interaction model comprises minimum data sets interacted among service scenes and a starting point service scene and an ending point service scene of the minimum data sets.
The data interaction model is used for defining the data logic relation between service scenes, defining the start point and the end point of data by taking the minimum data set as a unit, and providing reference for data interaction between scenes. Table 3 shows exemplary partial data minima in the data interaction model.
In a specific embodiment, the construction of the data interaction model may include the following steps:
step one, analyzing a first data set and a second data set which are needed and can be provided by each standard business scene. The step takes a business scene as a processing dimension, and extracts general data (or understood as typical data) which can be provided by each data source and needed general data respectively to form a total set of data resources. For ease of distinction and description, the data sets that can be provided by each business scenario are referred to as first data sets, and the data sets that are required by each business scenario are referred to as second data sets. Optionally, the main functions of each service scene are determined first, then the process data for realizing each main function is extracted, and the first data set and the required second data set which can be provided by each service scene are crawled from the process data. For example, the service scenarios in the highway full life cycle generally correspond to respective service systems, for example, the highway construction scenario corresponds to the highway construction system, and the road government management scenario corresponds to the road government management system, so that the main function modules of each service system can be called, the input and output files of each function module are extracted, and the first data set and the second data set of each service scenario are crawled from each input and output file. Taking the two-level business scene large-piece transportation management as an example, the main functional modules of the large-piece transportation management system, namely 'large-piece transportation permission application and approval', 'infrastructure structure security check', 'field check', 'en-route check', and 'highway traffic detection', are invoked, the input files and the output files of the functional modules are extracted, weighing detection information, maintenance construction blocking information, ETC portal information and the like are crawled out from the output files to jointly form a first data set of the large-piece transportation management scene, and carrier information, large-piece transportation permission application form information, supervision station basic information and the like are crawled out from the input files to jointly form a second data set of the large-piece transportation management scene. Table 4 shows exemplary first data sets and second data sets that may be provided by three business scenarios, bulk transportation management, illicit overrun transportation management, road production right of way management.
And step two, taking the intersection of each first data set and each second data set as the minimum data set of interaction between business scenes. The step can provide data and demand data for each business scene to obtain intersection sets as basic units for the subsequent traceability data sources. Analyzing the data sources in units larger than the minimum data set, wherein the risk of unclear analysis and incapability of tracing exists; analyzing the data sources in units smaller than the minimum data set may result in over analysis and redundant processing. Optionally, firstly, performing semantic disambiguation on the data in each first data set and each second data set, and then, for each two service scenes, taking an intersection of the first data set after disambiguation of one service scene and the second data set after disambiguation of the other service scene to obtain the minimum data set of the current two service scenes. The semantic disambiguation can unify data with the same content but different names into the same data name. The minimum data set obtained by the intersection mode can be expressed as:
in the formula (1),iandjrespectively representing the index of the traffic scene,is the firstiSecond data set of individual business scenario (disambiguated), +.>Is the firstjA first data set of a business scenario (disambiguated).
And thirdly, determining the source of each minimum data set, and constructing a data interaction model of the standard service scene set according to the source of each minimum data set. In the step, each minimum data set is taken as a basic unit, the interaction starting point and the interaction end point of each minimum data set are respectively determined, and the data items, the starting point and the end point of the minimum data set jointly form a data interaction model of the whole life cycle of the highway. Optionally, for any minimum data set, taking at least one service scene providing the minimum data set as a starting point and taking at least one service scene requiring the minimum data set as an end point; and combining the plurality of minimum data sets with the same starting point and end point to obtain a final data interaction model. Specifically, when merging multiple minimum data sets, in addition to merging data items included in each minimum data set, data descriptions corresponding to each data item may be merged to form a description of a new data unit. Fig. 2 shows a data interaction model in the form of a data stream, wherein an oval icon represents a traffic scenario involved in the full life cycle of a road, a box icon represents a third party mechanism involved in the full life cycle of a road, text on an arrow line represents the smallest data set (i.e. data item) of the data interaction model, and the arrow direction indicates the start and end of each data item.
S130, screening minimum data sets taking each actual service scene as a starting point service scene and/or an end point service scene from the data interaction model, and forming a data interaction sub-model of the actual service scene set by the screened minimum data sets, the starting point service scene and the end point service scene.
For ease of understanding and description, the data interaction model is represented herein as a collection of data items,which is provided withIn,iindex representing data entry->Representing the origin business scenario, & lt + & gt>Representing an end point business scenario>Representing the smallest dataset,/->Constitute the firstiA stripe data entry. Meanwhile, the actual business scene set is expressed as +.>This step is then derived from the data interaction model +.>Middle screening contains business scene set +.>(origin business scenario)Or end point business scenario->) Data subset +.>As->Corresponding data interaction sub-models. The model can limit the service scene range and the data range of the digitized evaluation in the embodiment, and the specific expression is as follows:
s140, calculating the scores of all evaluation indexes based on the data interaction sub-model; and constructing a judgment matrix by taking each evaluation index as a dimension, and executing a hierarchical analysis method based on the judgment matrix to determine the weight of each evaluation index.
In this step, each index score is calculated according to the service scene range and the data range defined in S130, the service scene outside the range does not belong to the focus of attention, the data outside the range does not involve interaction between scenes, the application value in the whole road network is limited, and the service scene outside the range can be excluded from the evaluation range.
In one embodiment, considering infrastructure data penetration in different traffic scenarios, the following indicators may be used to evaluate the degree of digitization of the full life cycle of the infrastructure, including: data value, sharing, compliance, uniqueness, stability and validity, for guiding infrastructure donor data identification, supporting shared application of infrastructure data among different business scenarios. Table 5 exemplarily shows the following partial evaluation indexes of the digitization degree of the infrastructure under a certain actual service scenario:
the frequent use, the frequent modification, the long-term preservation and the like can be defined according to the use condition of data in a business scene, for example, the use time is more than or equal to 1 time/day, the modification time is more than or equal to 3 times/month, the preservation time is more than or equal to half a year and the like.
Based on the evaluation index system, the scores of the indexes can be calculated by the following modes: merging the business scene in the data interaction sub-model with the minimum data set; and calculating the scores of all evaluation indexes according to the proportion of the data of all the attributes in the combined data set in the total data set of the combined service scene set. In particular, it is possible toAnd->Or->All->Taking the union set to form a combined data setDThe method comprises the steps of carrying out a first treatment on the surface of the Will->All->And->Taking the union set to form a business scene set after combination +.>And will->The total set of data related to each business scenario is recorded as +.>. Taking the secondary evaluation index "data importance" in Table 5 as an example, the extraction is performedDThe necessary data quantity ∈ ->And->Total number of data in (a)YWill be proportional toX/YScore as degree of importance of datag. Similarly, when the number of the evaluation indexes isNIn the case of (1), for each evaluation index +.>The corresponding score +.>。
Meanwhile, the embodiment calculates the weight of each evaluation index by adopting a hierarchical analysis method, and builds a contrast matrix by taking the data interaction sub-model and the data interaction model of the actual service scene set as dimensions in the hierarchical analysis method, so as to emphasize the importance of service scenes and data interactions in evaluation.
In a specific embodiment, the process of determining the weights of the evaluation indexes may include the following steps:
step one, constructing a judgment matrix by taking each evaluation index as a dimension, wherein the judgment matrix is used for reflecting the relative importance degree between every two evaluation indexes. Optionally, first construct a scale table for any two indicesAnd->The relative importance of (2) is quantized, and the quantized values "1,3,5,7,9" may respectively represent the index +.>And->The quantized values of the "equal importance, slightly equal importance, stronger importance, hotness importance and extreme importance" can be respectively "2,4,6 and 8" as the median value of the adjacent judgment. Correspond to->. Wherein, the first in the matrix dimensioniIndividual evaluation index->And (d)jIndividual evaluation index->Comparison value betweenThe calculations are as follows:
step two, dividingAnd analyzing whether the judgment matrix meets the consistency requirement or not, and if the judgment matrix does not meet the consistency requirement, reconstructing the judgment matrix until the consistency requirement is met. Exemplary, when the coherence ratioMeeting the consistency requirement. If not, re-scoring the decision matrix, i.e. re-acquiring +.>Until the consistency requirement is met. Specifically, the consistency ratio is based on the consistency index +.>Random concordance index->And judging the maximum eigenvalue of the matrix +.>And (3) calculating to obtain:
wherein,the random consistency index can be obtained by solving a normalized judgment matrix>Can be obtained by looking up a table according to the index number N. The specific process is the prior art and will not be described in detail.
And thirdly, calculating the weight corresponding to each evaluation index by utilizing a judgment matrix meeting the consistency requirement. Optionally, for a judgment matrixAfter normalization, each evaluation index ++was calculated by arithmetic average>Corresponding weight->The calculation is as follows:
and S150, carrying out weighted average on the scores and the weights of all evaluation indexes to obtain the digital degree evaluation value of the road infrastructure under the actual service scene set. Score using each evaluation index determined in S140And the weight of each evaluation index +.>Can obtain the scene set aiming at the actual businessSThe evaluation function of the highway infrastructure digitization level is as follows:
wherein,a value in between; />The greater the number of (c) the greater the degree of digitization. Can be according to->Dividing full life cycle digitization degree into three stages of high, medium and low, wherein ∈>And->For the shift threshold:
furthermore, the evaluation result can be used for providing basis for constructing the donor data for the road full life cycle foundation. For example, the data strategies of infrastructure without modification, partial modification and reconstruction can be respectively adopted for the high, medium and low three gears, so as to support the data through sharing among different business scenes.
In addition, on the basis of the embodiment, a deep learning model capable of automatically evaluating the digitization degree can be constructed by learning the data rule between each evaluation index score and the final evaluation value by means of the deep learning model. In a specific embodiment, a plurality of actual service scene sets to be evaluated can be obtained; after executing S120, executing S130-S150 on each actual service scene set respectively to obtain digital degree evaluation values of road infrastructure under each actual service scene set respectively; and training the decision tree model by taking the scores of all evaluation indexes corresponding to all the actual service scene sets as inputs, so that the output of the model is continuously approximate to the digital degree evaluation values corresponding to all the actual service scene sets. Table 6 shows, by way of example, the data content of the training samples described above, wherein,Mrepresenting the number of actual traffic scenario sets (i.e. the number of samples),representing actual business scenario setsmCorresponding evaluation index->Score of->Representing actual business scenario setsmCorresponding final evaluation value:
the trained decision tree model scores each evaluation index under the actual service scene setFor input, the evaluation value +.>For output, it can be used to evaluate the digitization degree of highway infrastructure under any actual service scene set. Specifically, after a new actual service scene set to be evaluated is obtained, each minimum data set taking each new actual service scene as a starting point service scene and/or an end point service scene is screened from the data interaction model, and the screened minimum data sets, the starting point service scene and the end point service scene together form a new data interaction sub-model of the new actual service scene set. Then, calculating new scores of all evaluation indexes based on the business scenes and the data in the new data interaction sub-model; and inputting the new scores of the evaluation indexes into a trained decision tree model to obtain the digital degree evaluation value of the road infrastructure under the new actual service scene set.
The embodiment provides a highway infrastructure digital degree evaluation method aiming at a service scene, which takes the service scene and data interaction as constraint conditions, and realizes accurate evaluation of the highway infrastructure full life cycle digital degree. Firstly, determining a general standard service scene set in the field of highway infrastructure according to related standards; determining a universal service scene range and a universal data range related to data interaction by analyzing a data interaction model of a standard service scene; and selecting a data range related to the actual service scene to be evaluated from the universal range, and calculating the scores of various evaluation indexes according to the data proportion of various attributes in the data range. Then, a judgment matrix is constructed by taking each evaluation index as a dimension, and the weights of each evaluation index are determined by executing a analytic hierarchy process. The index score and the weight determined by the method can highlight the importance of the service scene and the data interaction in the data evaluation, and reflect the application effect of the data resource based on the data interaction under the specific service scene set. Finally, through the digital degree evaluation value obtained by the score and weight calculation of each index, supporting the data penetration of the infrastructure data in different business scenes, guiding the data base establishment in the basic set donor data identification and digital transformation, and the like.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, 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 may be one or more, one processor 60 being taken as an example in fig. 3; the processor 60, the memory 61, the input means 62 and the output means 63 in the device may be connected by a bus or other means, in fig. 3 by way of example.
The memory 61 is used as a computer readable storage medium for storing a software program, a computer executable program and a module, such as a program instruction/module corresponding to the highway infrastructure digital degree evaluation method for a traffic scene in the embodiment of the present invention. The processor 60 executes various functional applications of the device and data processing by running software programs, instructions and modules stored in the memory 61, i.e. implements the above-described highway infrastructure digitization degree assessment method for business scenarios.
The memory 61 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 61 may further comprise memory remotely located relative to processor 60, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 62 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output 63 may comprise a display device such as a display screen.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the highway infrastructure digitization degree assessment method for the business scenario of any embodiment.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may 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, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the C-programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims (10)
1. The highway infrastructure digital degree evaluation method for the business scene is characterized by comprising the following steps of:
s110, acquiring a standard service scene set related to highway infrastructure and an actual service scene set to be evaluated;
s120, analyzing a data interaction model of the standard service scene set, wherein the data interaction model comprises minimum data sets interacted among service scenes and a starting point service scene and an ending point service scene of the minimum data sets;
s130, screening minimum data sets taking each actual service scene as a starting point service scene and/or an end point service scene from the data interaction model, and forming a data interaction sub-model of the actual service scene set by the screened minimum data sets, the starting point service scene and the end point service scene;
s140, calculating the scores of all evaluation indexes based on the data interaction sub-model; constructing a judgment matrix by taking each evaluation index as a dimension, and executing a analytic hierarchy process based on the judgment matrix to determine the weight of each evaluation index;
and S150, carrying out weighted average on the scores and the weights of all evaluation indexes to obtain the digital degree evaluation value of the road infrastructure under the actual service scene set.
2. The method according to claim 1, wherein the obtaining the standard traffic scenario set related to the highway infrastructure and the actual traffic scenario set to be evaluated includes:
determining general infrastructure and various general service scenes of the whole life cycle of the highway according to the related standards; screening all service scenes related to the universal infrastructure from all the universal service scenes to form a standard service scene set;
and acquiring the actual infrastructure of the road to be evaluated and each related actual service scene, and forming an actual service scene set by each actual service scene.
3. The method of claim 1, wherein said analyzing the data interaction model of the standard set of business scenarios comprises:
analyzing a first data set and a second data set which are needed and can be provided by each standard business scene;
taking the intersection of each first data set and each second data set as the minimum data set of interaction between business scenes;
and determining the source of each minimum data set, and constructing a data interaction model of the standard service scene set according to the source of each minimum data set.
4. A method according to claim 3, wherein said determining the source of each minimum data set and constructing a data interaction model for said standard set of business scenarios based on the source of each minimum data set comprises:
taking a standard service scene capable of providing each minimum data set as a starting point service scene of each minimum data set;
taking the standard service scene requiring each minimum data set as the terminal service scene of each minimum data set;
and merging a plurality of minimum data sets with the same starting point service scene and end point service scene to obtain a data interaction model of the standard service scene set.
5. The method of claim 1, wherein calculating the score for each evaluation index based on the data interaction sub-model comprises:
merging the business scene in the data interaction sub-model with the minimum data set;
according to the proportion of the data of various attributes in the combined data set in the total data set of the combined service scene set, calculating the score of each evaluation index, wherein each evaluation index comprises: data value, shareability, compliance, uniqueness, stability, and validity.
6. The method of claim 1, wherein constructing a decision matrix with each evaluation index as a dimension, performing a hierarchical analysis based on the decision matrix, and determining weights of each evaluation index comprises:
constructing a judgment matrix by taking each evaluation index as a dimension, wherein the judgment matrix is used for reflecting the relative importance degree between every two evaluation indexes;
analyzing whether the judgment matrix meets the consistency requirement or not, and if the judgment matrix does not meet the consistency requirement, reconstructing the judgment matrix until the consistency requirement is met;
and calculating the weight corresponding to each evaluation index by using a judgment matrix meeting the consistency requirement.
7. The method according to claim 1, wherein the actual service scene set to be evaluated is a plurality of;
after S120, the method further includes:
S130-S150 are respectively executed on each actual service scene set, and digital degree evaluation values of road infrastructure under each actual service scene set are respectively obtained;
and training the decision tree model by taking the scores of all evaluation indexes corresponding to all the actual service scene sets as input, so that the output of the model is continuously approximate to the digital degree evaluation values corresponding to all the actual service scene sets.
8. The method according to claim 7, wherein after training the decision tree model with the score of each evaluation index corresponding to each actual service scene set as an input so that the output of the model continuously approximates to the digitalized degree evaluation value corresponding to each actual service scene set, further comprising:
acquiring a new actual service scene set to be evaluated;
screening each minimum data set taking each new actual service scene as a starting point service scene and/or an end point service scene from the data interaction model, and forming a new data interaction sub-model of the new actual service scene set by the screened each minimum data set, the starting point service scene and the end point service scene;
calculating new scores of all evaluation indexes based on the new data interaction sub-model;
and inputting the new scores of the evaluation indexes into a trained decision tree model to obtain the digital degree evaluation value of the road infrastructure under the new actual service scene set.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the highway infrastructure digitizing level assessment method for traffic scenarios of any of claims 1-8.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the highway infrastructure digitization level assessment method for a business scenario of any of claims 1-8.
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| US20080040364A1 (en) * | 2007-05-29 | 2008-02-14 | Di Li | Extensible multi-dimensional framework |
| CN114021971A (en) * | 2021-11-04 | 2022-02-08 | 长安大学 | Comprehensive evaluation system, method and storage medium for expressway operation and maintenance management |
| CN116467153A (en) * | 2022-01-11 | 2023-07-21 | 腾讯科技（深圳）有限公司 | Data processing method, device, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20080040364A1 (en) * | 2007-05-29 | 2008-02-14 | Di Li | Extensible multi-dimensional framework |
| CN114021971A (en) * | 2021-11-04 | 2022-02-08 | 长安大学 | Comprehensive evaluation system, method and storage medium for expressway operation and maintenance management |
| CN116467153A (en) * | 2022-01-11 | 2023-07-21 | 腾讯科技（深圳）有限公司 | Data processing method, device, computer equipment and storage medium |
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