CN117745124B - Intelligent expressway evaluation method and device - Google Patents
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Abstract
The embodiment of the application discloses an intelligent expressway evaluation method and device, wherein the method comprises the following steps: determining an evaluation object and a reference object; acquiring basic data for evaluation; constructing an evaluation model, wherein an evaluation index system of the evaluation model comprises three levels, namely a first-level index, a second-level index and a third-level index, wherein the first-level index comprises the intelligent sensing and service capability of an outfield, intelligent application, innovation application, development guarantee and comprehensive effect, each first-level index comprises at least one second-level index, each second-level index comprises at least one third-level index, and the indexes of each level respectively have corresponding weighting, scoring range and scoring rule of the third-level index; and determining the comprehensive score of the evaluation object according to the evaluation model. The application integrates mass multisource data of the expressway, provides a method and a device capable of quantitatively evaluating the construction benefits of the intelligent expressway, is beneficial to screening effective intelligent highway construction scenes to be popularized and applied in a large scale, and avoids blindness of intelligent highway construction.
Description
Technical Field
The application relates to the technical field of computers, in particular to an intelligent expressway evaluation method and device.
Background
With the increasingly tight constraints of land, energy, environment, funds and the like, the requirements of improving the bearing capacity and service quality of the expressway infrastructure by fully utilizing the new generation information technology and realizing intelligent capacity expansion are more and more urgent, and the intelligent expressway is created. The intelligent expressway aims at improving the safety, convenience, high efficiency, green and economic service level of the expressway, enables the expressway to be realized by using a modern information technology, and realizes the novel expressway with full-service full-period digitization, networking and intellectualization of construction, management, maintenance, operation and service. The intelligent expressway construction is subjected to omnibearing quantitative evaluation, so that effective intelligent expressway construction scenes are screened to be popularized and applied in a large scale and standardized mode, blind construction of the intelligent expressway is avoided, and sustainable development power of the intelligent expressway is stimulated.
Disclosure of Invention
The embodiment of the application provides an intelligent expressway evaluation method and device, which can objectively evaluate an intelligent expressway.
In a first aspect, an embodiment of the present application provides a method for evaluating an intelligent expressway, including:
the intelligent expressway evaluation method comprises the following steps:
Determining an evaluation object and a reference object, wherein the reference object comprises a first reference object and a second reference object, the first reference object is a common high-speed road section with equal traffic flow, similar weather conditions and vehicle type structures and the same lane number relative to the evaluation object, and the second reference object is a common high-speed road section before intelligent modification of the evaluation object;
Acquiring basic data for evaluation, wherein the basic data comprises construction engineering data, road operation data, operation data and satisfaction survey data;
An evaluation model is built, an evaluation index system of the evaluation model comprises three levels, namely a first level index, a second level index and a third level index, wherein the first level index comprises the intelligent sensing and service capability of an outfield, intelligent application, innovation application, development guarantee and comprehensive effect, each first level index comprises at least one second level index, each second level index comprises at least one third level index, and the indexes of each level respectively have corresponding weighting, scoring range and scoring rules of the third level index;
And determining the comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and the third-level index score is obtained by basic data and a scoring rule.
In an alternative embodiment, constructing the evaluation model includes:
Constructing an evaluation index system;
determining index weights by adopting expert scoring and a analytic hierarchy process;
And determining full score values of the secondary index and the tertiary index step by taking the primary index as a reference according to the index weight and the score value of the corresponding previous index, wherein the score range of each tertiary index is 0 to full score.
In an alternative embodiment, the index weight is determined by expert scoring in combination with analytic hierarchy process. In order to eliminate the influence of subjective factors of an expert scoring method on results to the greatest extent, the weights of all indexes are obtained more accurately and objectively, and the invention creatively provides a more scientific and reasonable weight calculation method. The method comprises the following steps that firstly, the expert ranks all indexes according to the importance from high to low, on the basis, importance differences between adjacent indexes are ranked in sequence, and then the importance of the indexes is determined by integrating results given by multiple experts. Finally, constructing a judgment matrix to determine index weight, wherein the specific method and the specific steps are as follows:
All indexes belonging to the same level of the same upper level index are ranked by m industry experts, the more important index ranking orders are more forward, the importance distances of two adjacent indexes are sequentially ranked, the importance distances are divided into a plurality of levels from high to low, the importance distances are respectively represented by numerical values, and the higher the importance distances are, the larger the corresponding numerical values are;
The importance of each index is determined by synthesizing the results given by m-bit experts, and the calculation formula is as follows:
Wherein:
b j denotes an importance score of the index j;
a ij represents the ranking given by expert i for index j, if index j ranks 1, a ij is 1;
s ij represents the importance difference rating between the index j given by the expert i and the index of the next rank, and the importance difference rating for the last index in the ranking is 0;
m represents expert number;
n represents the number of the same level index belonging to the same upper level index;
constructing a judgment matrix according to the importance scores of the indexes, and calculating formulas such as formula (2) and formula (3):
w ij represents the ratio of importance scores of the two indexes i and j, and is an element for constructing a judgment matrix, i and j epsilon (1-n);
n represents the number of the same level index belonging to the same upper level index;
calculating a consistency check index CR by using the formula (4) and the formula (5), if CR is smaller than a threshold value, the consistency passes the requirement,
Wherein:
lambda max represents the maximum eigenvalue of the judgment matrix;
n represents the number of the same level index belonging to the same upper level index;
The RI value is obtained by a consistency check table;
if the judgment matrix passes the consistency test, the feature vector (w 1,w2,…,wn)T represents the weight of each index) corresponding to the judgment matrix is normalized to obtain the weight value of each index.
In an alternative embodiment, deriving a three-level indicator score from the base data and the scoring rules includes:
Designing three-level indexes subordinate to the intelligent sensing and service capability, intelligent application, innovation application and development guarantee of the outfield into open indexes, defining initial scoring rules according to construction contents, determining initial scores of the three-level indexes according to the initial scoring rules, and marking the initial scores as technical compliance scores;
Correcting initial scores of three-level indexes of the intelligent sensing and service capability, the intelligent application and the innovation application of the outfield according to actual construction conditions, and marking the corrected scores as actual scores of the indexes;
Setting the three-level index subordinate to the comprehensive effect as an objective index, and calculating the actual score according to the basic statistical data.
In an alternative embodiment, the actual score of the third-level index of the outfield intelligent perception and service capability is equal to the product of the initial score, the coverage degree coefficient and the reliability coefficient, and the calculation is performed according to a formula (6):
Fk＝fk×αk×βk (6)
Wherein:
f k, the actual score of the kth three-level index;
f k -the technical compliance score of the kth three-level index, which is determined according to the technical compliance score rule;
Alpha k, namely a coverage degree coefficient of a kth three-level index, taking a value according to the proportion of highway mileage meeting the technical compliance requirement to the total mileage of the project, or the proportion of bridge/tunnel linear rice meeting the requirement to the total linear rice, or the proportion of the number of service areas meeting the requirement to the total number of the service areas, wherein the coefficient takes 1 when the value of the proportion is more than or equal to 80%; when the ratio is more than or equal to 50% and less than 80%, the coefficient is 0.7; when the ratio is less than 50%, the coefficient is 0.5;
beta k, namely the reliability coefficient of the kth three-level index, taking a value according to the average online rate of various intelligent sensing and service facility equipment, wherein the average online rate is the ratio of the online time of the equipment which works normally to the total time, and the reliability coefficient is 1 when the average online rate is more than or equal to 90%; when the average online rate is more than or equal to 75% and less than 90%, the reliability coefficient is 0.7; and when the average linear rate is less than 75%, the reliability coefficient is 0.5.
In an alternative embodiment, the actual score of the three-level index of the intelligent application is equal to the product of the technical compliance score and the system usage degree coefficient, and the calculation is performed according to the formula (7):
Fk＝fk×δk (7)
Wherein:
f k, the actual score of the kth three-level index;
f k -a technical compliance score for the kth tertiary index;
delta k -a system use degree coefficient corresponding to the kth three-level index, taking 1 according to the liveness value of various system users, wherein the liveness is the proportion of the number of daily active users actually logging in and using the system to the number of users with opening permission, and the liveness is more than or equal to 80%; when the activity is more than or equal to 50% and less than 80%, the coefficient of the system use degree is 0.7; and when the activity is less than 50%, the system use degree coefficient is 0.5.
In an alternative embodiment, the actual score of the three-level index of the innovative application is comprehensively scored and modified according to the type, scale and public service experience of the innovative application, wherein each innovative application which is not listed in the existing scoring rules in the table and is proved to be effective through practice can be incorporated into the index of the innovative application to calculate the score.
In an alternative embodiment, the three-level index subordinate to the comprehensive effect is an objective index, and the score is obtained by direct comparison calculation by selecting a reference object based on road running state data, operation data and satisfaction questionnaire data.
In an alternative embodiment, the composite score is calculated according to equation (8), equation (9) and equation (10):
Wherein:
F represents the comprehensive score of the intelligent expressway construction effect evaluation;
F i represents each first-level index score of the intelligent high-speed construction effect evaluation;
omega i represents the weight of the ith primary index F i, and the sum of the weights of all primary indexes is equal to 1;
f ij represents the actual score of the j-th secondary index set under the primary index F i;
f ijk represents the actual score of the kth third-level index set under the second-level index F ij;
x represents the number of all three-level indexes arranged under each two-level index;
y represents the number of all secondary indexes set under each primary index.
In a second aspect, an embodiment of the present application provides an intelligent highway evaluation apparatus, including:
The system comprises a determining module, a judging module and a judging module, wherein the judging module is used for judging whether the evaluation object is an ordinary high-speed road section or not according to the vehicle type structure, the weather condition and the vehicle type structure of the evaluation object, and the lane number of the evaluation object is the same as that of the reference object;
The data module is used for acquiring basic data for evaluation, wherein the basic data comprises construction engineering data, road operation data, operation data and satisfaction survey data;
The computing module is used for constructing an evaluation model, wherein an evaluation index system of the evaluation model comprises three levels, namely a first-level index, a second-level index and a third-level index, the first-level index comprises the external intelligent sensing and service capability, intelligent application, innovative application, development guarantee and comprehensive effect, each first-level index comprises at least one second-level index, each second-level index comprises at least one third-level index, and the indexes of each level respectively have corresponding weighting, scoring range and scoring rules of the third-level index;
And determining a comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and a third-level index score is obtained by basic data and a scoring rule.
In a third aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as claimed in any preceding claim.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method of any one of the above when executing the computer program.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent highway evaluation method according to an embodiment of the present application;
FIG. 2 shows a diagram of an evaluation result obtained by applying the intelligent expressway evaluation method according to the embodiment of the application;
FIG. 3 is a schematic diagram showing the construction of an intelligent highway evaluation apparatus according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to better understand the solution of the present application, the following description will clearly and completely describe the solution of the embodiment of the present application, and it is obvious that the described embodiment is only a part of the embodiment of the present application, not all the embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, an embodiment of the present application provides an intelligent expressway evaluation method, including:
S1, determining an evaluation object and a reference object, wherein the reference object comprises a first reference object and a second reference object, the first reference object is a common high-speed road section corresponding to the evaluation object, the vehicle flow is equivalent, the climate conditions are similar to the vehicle type structure, and the number of lanes is the same, and the second reference object is a common high-speed road section before intelligent modification of the evaluation object;
S2, acquiring basic data for evaluation, wherein the basic data comprise construction engineering data, road operation data, operation data and satisfaction survey data;
S3, constructing an evaluation model, wherein an evaluation index system of the evaluation model comprises three levels, namely a first level index, a second level index and a third level index, wherein the first level index comprises the intelligent sensing and service capability of an outfield, intelligent application, innovative application, development guarantee and comprehensive effect, each first level index comprises at least one second level index, each second level index comprises at least one third level index, and the indexes of each level respectively have corresponding weight, scoring range and scoring rule of the third level index;
S4, determining the comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and the third-level index score is obtained by basic data and a scoring rule.
The evaluation method provided by the application has comprehensive and computable indexes, the evaluation model is scientific and operable, the research blank of quantitatively evaluating the construction effect of the intelligent expressway engineering is filled, and a quantitative reference basis can be provided for completion acceptance and assessment of the intelligent expressway engineering.
In the embodiment of the application, in order to ensure that the data is truly, accurately and obtainable, an evaluation object needs to complete intelligent highway engineering construction, and the built-up traffic operation is not less than 12 months (including a test operation period); or the existing expressway completes intelligent improvement engineering and is put into operation for at least 12 months (including test operation period).
When the evaluation object is a newly built intelligent expressway, a similar comparison method is adopted, and road sections with equivalent vehicle flow, similar weather conditions and vehicle type structures and same lane number are selected as reference objects. When the evaluation object is a reconstruction expressway, a front-back comparison method is adopted, and the road section is selected as a reference object before construction.
Considering the variability of highways, it is necessary to perform classification evaluation according to the functional location and service level of the highways. The application classifies highways into two categories: class I highways include highways within the main skeleton of national integrated stereoscopic traffic networks, important highways within urban areas, and highways with average daily annual saturation (v/C) greater than 0.55 and other average daily annual saturation (v/C) greater than 0.75. Class II highways refer to other highways than class I.
The application needs to collect the following four types of data: firstly, materials such as intelligent highway engineering supervision, design, construction, user use condition, third party software test, main outfield facility quality performance detection and the like. And secondly, road operation data such as expressway traffic, average speed, accident rate, congestion condition, emergency rescue condition and the like. And thirdly, operation data such as expressway charging, maintenance, road sealing, energy consumption and electricity consumption. Fourth, satisfaction survey data.
The collected basic data can be preprocessed according to an index calculation formula and a data cleaning rule.
In some embodiments, the base data preprocessing specifically includes at least one of the following steps:
s21, aiming at equipment technical parameter indexes, determining key technical parameter indexes and threshold ranges of different types of equipment.
S22, aiming at expressway road operation data, eliminating missing, abnormal and repeated data of key data fields; filling the missing value by adopting data such as null value, none or median, and the like, and finally carrying out association matching on the data and the road section.
S23, firstly removing invalid data of which more than 60% of questions are not answered and repeated answering questionnaire data, verifying MAD (absolute median difference) outlier, IQR (quarter bit interval) outlier and 3slgma outlier, and finally converting questionnaire coding data and converting questions in the questionnaire into numerical values capable of carrying out operation. In some embodiments, constructing the evaluation model includes:
S31, constructing an evaluation index system: by comprehensively considering the construction characteristics of the intelligent expressway, the embodiment of the application determines evaluation indexes from 5 aspects of intelligent sensing and service capability of the outfield, intelligent application, innovation application, development guarantee and comprehensive effect, and 16 secondary indexes and 30 tertiary indexes are set in total, and the detailed table 1 is shown.
Table 1 evaluation index of Intelligent expressway construction effect
S32, determining index weights by adopting expert scoring and a analytic hierarchy process;
And determining full score values of the secondary index and the tertiary index step by taking the primary index as a reference according to the index weight and the score value of the corresponding previous index, wherein the score range of each tertiary index is 0 to full score.
In some embodiments, determining the index weight using expert scoring in combination with analytic hierarchy process includes: and determining the index weight step by step according to the sequence of the layers from top to bottom.
The application can definitely evaluate the target and the hierarchical structure, and mainly comprises the following steps:
S321, clearly evaluating the target. The evaluation target of the method is to quantitatively evaluate the construction effect of the intelligent highway engineering, find problems and comb experience, and provide reference and decision support for engineering acceptance and improvement.
S322, carding the hierarchical structure. The top layer is generally an evaluation target, and a criterion layer (first-level index), a sub-criterion layer (second-level index) and a scheme layer (third-level index) are sequentially arranged below.
Determining index weights step by step according to the sequence of the layers from top to bottom, wherein the index weights for the same level comprise:
S323, sorting all indexes of the same level, which belong to the same upper level index, by m industry experts, wherein in specific implementation, sorting is performed according to the importance degree from high to low, the more important index sorting orders are more forward, the importance distances of two adjacent indexes are sequentially graded, the experts can grade the importance distances of the two adjacent indexes according to professional cognition, the importance distance grading score can take a value from a set value range, the larger the distance is, the higher the grading score is, and the importance distance grading value of the last index in sorting can be the lowest value in the set value range. The specific value of the importance gap rank score may be an integer. In an exemplary embodiment, the importance differences may be divided into several levels from high to low, and respectively expressed in terms of values, with the larger the importance differences, the larger the corresponding values. For example, the importance level can be classified into three levels of importance, and general, from high to low, and is represented by the values 3, 2, and 1, respectively. Of course, the importance gaps may also be classified into more levels, for example, 5 levels, 6 levels, 7 levels, 9 levels, and the like. And determining the importance of each index by comprehensively considering expert sequencing and importance gaps.
And on the basis, the importance scores of the indexes are determined by combining the results given by m experts. The calculation formula is as follows (1):
Wherein:
b j represents the importance of the index j;
a ij represents the ranking order given by expert i for index j, if index j ranks to 1, a ij is 1, and is most important in the same level of index;
s ij represents the importance margin rating between the index j considered by the expert i and the index of the next rank, for example, s ij is 3, and then represents that the index j is very important compared with the index of the next rank, and the importance margin rating for the last index in the ranking may be 0;
m represents expert number;
n represents the number of the same level index belonging to the same upper level index;
s324, constructing a judgment matrix according to the importance scores of the indexes, wherein the calculation formulas are shown as formula (2) and formula (3):
w ij represents the ratio of importance scores of the two indexes i and j, and is an element for constructing a judgment matrix, i and j epsilon (1-n);
n represents the number of the same level index belonging to the same upper level index.
S325, consistency test. Calculating a consistency check index CR by using a formula (4) and a formula (5), if CR is smaller than a threshold value, the consistency passes the requirement, the threshold value can be specifically 0.1, namely, if CR is smaller than 0.1, the consistency passes the requirement,
Wherein:
lambda max represents the maximum eigenvalue of the judgment matrix;
n represents the number of the same level index belonging to the same upper level index;
the RI value is obtained from a consistency check table.
RI is given in table 2, e.g., RI equals 0.90 when n=4.
Table 2 consistency check table
| Order (n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
S326, determining index weights. If the judgment matrix passes the consistency test, the feature vector corresponding to the judgment matrix (w 1,w2,…,wn)T can represent the weight of each index).
In the embodiment of the application, when the full score of each index is determined, the full score of the first-level index is uniformly recorded as 100 points, and the full scores of the indexes of the same level belonging to the same upper-level index are determined according to the weights of the indexes and the full scores of the corresponding upper-level indexes. For example, the secondary indexes belonging to the same primary index determine respective full score values according to respective weights and full score values of corresponding primary indexes, and the tertiary indexes belonging to the same secondary index determine respective full score values according to respective weights and full score values of corresponding secondary indexes.
In the embodiment of the application, in order to reflect the construction effect of the intelligent expressway more truly, objectively and accurately and fully consider the ground variance, the application designs three-level indexes subordinate to four primary indexes of intelligent sensing and service capability, intelligent application, innovation application and development guarantee of the outfield into open indexes from the aspect of functions, and determines an initial scoring rule according to construction contents. Materials such as engineering supervision, design, construction, user use condition, third party software test, main outfield facility quality performance detection and the like are main basic data of the openness index.
In some embodiments, deriving a three-level indicator score from the base data and the scoring rules includes:
The three-level indexes subordinate to the intelligent sensing and service capability, the intelligent application, the innovation application and the development guarantee of the outfield are designed into open indexes, an initial scoring rule is defined according to construction content, and the initial score of the three-level indexes is determined according to the initial scoring rule and is recorded as a technical compliance score. The method comprises the steps of firstly setting initial scoring rules of each index according to factors such as the current development situation of an intelligent highway, actual demands, expert suggestions and important construction contents, obtaining corresponding technical compliance scores if a certain function or intelligent application scene reaches corresponding technical indexes, and not scoring or scoring lower if the certain function or intelligent application scene does not reach the corresponding technical indexes. The score range fully reflects the satisfaction degree difference of the scoring clauses, the lowest degree satisfies the scoring clauses to take the minimum value, the highest degree satisfies the maximum value, and the score range is between the lowest degree and the highest degree, and takes the intermediate value according to the satisfaction degree.
And correcting the initial scores of the three-level indexes of the intelligent sensing and service capability, the intelligent application and the innovative application of the outfield according to the actual construction condition, and marking the corrected scores as the actual scores of the indexes.
In some embodiments, the actual score of the third-level indicator of the outfield intelligent perception and service capability is equal to the product of the initial score and the coverage coefficient and reliability coefficient, and the calculation is performed according to the formula (6):
Fk＝fk×αk×βk (6)
Wherein:
f k, the actual score of the kth three-level index;
f k -the technical compliance score of the kth three-level index, which is determined according to the technical compliance score rule;
Alpha k, namely a coverage degree coefficient of a kth three-level index, taking 1 according to the ratio of highway mileage meeting the technical compliance requirement to total mileage of the project, or the ratio of bridge/tunnel linear rice meeting the requirement to total linear rice, or the ratio of the number of service areas meeting the requirement to the total number of service areas, wherein the ratio is more than or equal to 80 percent; when the ratio of the above values is more than or equal to 50% and less than 80%, the coverage degree coefficient is 0.7; when the ratio of the above-mentioned one is less than 50%, the coverage degree coefficient is 0.5;
beta k, namely the reliability coefficient of the kth three-level index, taking a value according to the average online rate of various intelligent sensing and service facility equipment, wherein the average online rate is the ratio of the online time of the equipment which works normally to the total time, and when the average online rate is more than or equal to 90 percent, the reliability coefficient is 1; when the average online rate is more than or equal to 75% and less than 90%, the reliability coefficient is 0.7; when the average linear rate is less than 75%, the reliability coefficient is 0.5.
In some embodiments, the actual score of the three-level indicator for the intelligent application is equal to the product of the technical compliance score and the system usage factor, calculated according to equation (7):
Fk＝fk×δk (7)
Wherein:
f k, the actual score of the kth three-level index;
f k -a technical compliance score for the kth tertiary index;
Delta k -a system use degree coefficient corresponding to the kth three-level index, taking 1 according to the activity degree value of various system users, wherein the activity degree is the proportion of the number of daily active users actually logged in and using the system to the number of users with opening permission, and the activity degree is more than or equal to 80%; when the activity is more than or equal to 50% and less than 80%, the system use degree coefficient is 0.7; when the activity is less than 50%, the system use degree coefficient is 0.5.
In some embodiments, the actual score of the tertiary index of the innovative application class is comprehensively scored and modified according to the type, scale and public service experience of the innovative application, wherein each innovative application which is not listed in the existing scoring rules and is effective through practice can be incorporated into the innovative application index to calculate the score.
In some embodiments, the three-level index subordinate to the comprehensive effect is set as an objective index, and the actual score is calculated according to the basic statistical data.
The three-level index subordinate to the comprehensive effect is an objective index, and the index score is obtained by directly comparing and calculating by selecting a reference object on the basis of road running state data, operation data and satisfaction questionnaire survey data.
In some embodiments, calculating a three-level indicator score for a composite effect subordinate includes:
Based on road running state data, operation data and satisfaction questionnaire data, objective indexes are calculated by analogy with a reference object.
In an exemplary embodiment, the index calculation steps are as follows:
Step1: and (5) calculating indexes. Objective indexes include traffic capacity, average speed, accident rate of thousands of vehicles and kilometers, emergency security efficiency, complaint rate, user satisfaction, green energy utilization rate, operation management cost, operation income level and the like. The data calculated by the index should be average number of years, and generally, report forms or data of three years are collected, for example, the data which can be collected is less than one year, and continuous data which is not less than 6 months should be selected.
Step2: a reference object is determined. The new expressway calculates index results by adopting a similar analogy method, and refers to the expressways (road sections) which are designed to have the same traffic capacity, similar traffic volume and passenger-cargo structure in the selected area and are not developed for intelligent expressway construction. The method comprises the steps of intelligently improving and reconstructing the expressway and the existing expressway, calculating index results by adopting a front-back analogy method, and obtaining the reference object, namely the operation state of the road at the same time period before engineering implementation.
Step3: the scoring rules are specified. The invention comprehensively considers the overall situation of the intelligent expressway built nationwide, and defines the scoring rule of each index, and the scoring rule is shown in Table 3.
Table 3 objective evaluation index calculation method
In some embodiments, the composite score is calculated according to equation (8), equation (9) and equation (10):
Wherein:
F represents the comprehensive score of the intelligent expressway construction effect evaluation;
F i represents each first-level index score of the intelligent high-speed construction effect evaluation;
omega i represents the weight of the ith primary index F i, and the sum of the weights of all primary indexes is equal to 1;
f ij represents the actual score of the j-th secondary index set under the primary index F i;
f ijk represents the actual score of the kth third-level index set under the second-level index F ij;
x represents the number of all three-level indexes arranged under each two-level index;
y represents the number of all secondary indexes set under each primary index.
In some embodiments, the evaluation method of the embodiments of the present application further includes: and determining the intelligent level of the expressway according to the comprehensive score.
The comparability among the intelligent highways is comprehensively considered, the difference of the characteristics of each place and each item is considered, and the classification is carried out by adopting a mode of combining the comprehensive score and the special score (taking the outfield perception and service capability, the intelligent application and the innovative application as the special score and comprehensively considering when the intelligent highways are classified, the development guarantee and the comprehensive effect can effectively embody comprehensive measures and effects without repeatedly taking into consideration). In practice, the intelligent highways are classified into 3 star class, 4 star class and 5 star class. As shown in table 4.
Table 4 Intelligent highway grade combination assessment method
The ranking of the composite score and the individual score is referred to in table 5.
TABLE 5 comprehensive score and specific score ranking rules
The method and effect of the present application will be described below by taking an example of evaluating a certain established intelligent highway section (hereinafter referred to as "m section") by applying the method of the present application.
The m road section is constructed by starting work in 2019 and is constructed by passing traffic in 2021 and 9 months, a large amount of actual operation data is accumulated, and real data support is provided.
The method of the embodiment of the application comprises the following steps:
step1, definitely evaluating an object;
The evaluation object of the embodiment is an m-link and is a newly built road, so that an n-link which is equivalent to the traffic flow of the m-link, has the same number of lanes and has similar weather conditions is selected by adopting a similar comparison method as a reference object to perform intelligent construction effect evaluation.
According to the classification principle, the m road sections belong to class I highways.
Step2, basic data collection;
The embodiment collects 4 kinds of data altogether, and the data are obtained through official and normal channels such as statistics departments, industry authorities, expressway operation units and the like, and are real and effective.
(1) Collecting process files of intelligent construction engineering of m road sections, including files such as wholesale, design, construction, user use reports, third party outfield design quality performance monitoring reports and the like;
(2) According to the reference object selection principle, the newly-built road adopts a similar comparison method to select n road sections as reference objects. Traffic, speed, accident, congestion, charging and other road operation state data of the m road sections 2021, 9 months and 2022, 9 months and other operation data of road maintenance, road sealing, electromechanical operation and maintenance and the like are collected. The data are directly exported by an informatization system or recorded by a line of staff, and the data granularity is fine and the authenticity is high.
Table 6 Portal flow data (partial data example)
Table 7 Portal speed data (partial data example)
TABLE 8 Congestion data (partial data example)
(3) Collecting operation data such as charging, road maintenance, road sealing, electromechanical operation and maintenance of m road sections and n road sections from 2021 year 9 month to 2022 year 9 month;
(4) In terms of service satisfaction, the embodiment designs questionnaires for information system users and drivers and passengers respectively, and collects 22 parts and 61 parts of effective questionnaires respectively.
Step 3, constructing an intelligent expressway construction effect evaluation model;
And marking and sorting the index importance according to 9 industry experts invited from government management units, expressway groups, road operation management units, high-efficiency and scientific research campuses, and determining the weight of each index. The full score of 5 primary indexes is set as 100, and the full scores of secondary and tertiary indexes are calculated according to the index weights of each stage, and are shown in Table 9.
Table 9 index weights and full score values for each level
And calculating the technical compliance score of the three-level index according to the step S34, and calculating the actual score of each index according to various index correction principles, wherein the result is shown in a table 10.
Table 10m section intelligent construction effect actual score
Step 4, determining an evaluation score;
according to the step 4, the comprehensive score of the intelligent construction of the m road sections is 83.03; wherein the offsite intelligence perception and service capability, wisdom application, innovation application, development assurance, and comprehensive effects scores are 75.5, 91, 95, 79, and 82, respectively.
Step 5: determining the intelligent grade of the expressway;
And calculating to obtain the comprehensive score of the m road sections as 4-star grade, wherein two of the three special scores are 5-star grade and are not lower than 3 grade. According to the intelligent highway grade combination assessment method (table 4) and the comprehensive score and special score grade division rule (table 5), the final intelligent grade of the m road sections is calculated to be 4 star grade, the result is detailed in table 11, and the graphic representation can be referred to in fig. 2.
Table 11m Intelligent grade of road section
According to the analysis result of the embodiment, the method can quantitatively analyze the construction effect of the intelligent expressway and define the intelligent level, and the evaluation result can provide data and theoretical support for the summary of the construction effect of the intelligent expressway, the problem analysis and the experience carding. The intelligent highway intelligent expansion system provides guiding reference significance for improvement and subsequent engineering construction, makes clear economic and social benefits, excites the endophytic power of the intelligent highway, guides enterprises to actively participate in construction, and lays a foundation for realizing intelligent expansion of traffic infrastructure. The method realizes quantitative evaluation and intelligent classification of the intelligent expressway construction effect.
The embodiment of the application provides an intelligent expressway evaluation device, which can realize the method of the embodiment, the embodiment of the method can be used for understanding the device of the embodiment of the application, and the description part of the embodiment of the device can also be used for understanding the method of the embodiment of the application.
Referring to fig. 3, an intelligent highway evaluation apparatus according to an embodiment of the present application includes a determining module, a data module, and a calculating module.
The determining module is used for determining an evaluation object and a reference object, wherein the reference object comprises a first reference object and a second reference object, the first reference object is a common high-speed road section which is corresponding to the evaluation object, has the same traffic flow, similar weather conditions and vehicle type structures and the same lane number, and the second reference object is a common high-speed road section before intelligent modification of the evaluation object;
the data module is used for acquiring basic data for evaluation, wherein the basic data comprise construction engineering data, road operation data, operation data and satisfaction survey data;
The computing module is used for constructing an evaluation model, an evaluation index system of the evaluation model comprises three levels, namely a first level index, a second level index and a third level index, wherein the first level index comprises the intelligent sensing and service capability of an outfield, intelligent application, innovative application, development guarantee and comprehensive effect, each first level index comprises at least one second level index, each second level index comprises at least one third level index, and the indexes of each level respectively have corresponding weight, scoring range and scoring rules of the third level index; and determining a comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and a third-level index score is obtained by basic data and a scoring rule.
An embodiment of the present application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing a method of any of the above when executing the computer program.
Referring to fig. 4, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 4, the terminal 600 may include: at least one processor 601, at least one network interface 604, a user interface 603, a memory 605, at least one communication bus 602.
Wherein the communication bus 602 is used to enable connected communications between these components.
The user interface 603 may include a Display screen (Display), a Camera (Camera), and the optional user interface 603 may further include a standard wired interface, a wireless interface.
The network interface 604 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 601 may include one or more processing cores. The processor 601 connects various parts within the overall terminal 600 using various interfaces and lines, performs various functions of the terminal 600 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and invoking data stored in the memory 605. Alternatively, the processor 601 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 601 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 601 and may be implemented by a single chip.
The Memory 605 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 605 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 605 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 605 may also optionally be at least one storage device located remotely from the processor 601. As shown in fig. 4, an operating system, a network communication module, a user interface module, and application programs may be included in the memory 605, which is one type of computer storage medium.
In the electronic device 600 shown in fig. 4, the user interface 603 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 601 may be operative to invoke application programs stored in memory 605 and to perform in particular the operations of any of the method embodiments described above.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer-readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above.
It will be clear to a person skilled in the art that the solution according to the application can be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a particular function, either alone or in combination with other components, such as Field programmable gate arrays (Field-ProgrammaBLE GATE ARRAY, FPGA), integrated circuits (INTEGRATED CIRCUIT, ICs), etc.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application. That is, equivalent changes and modifications are contemplated by the teachings of the present application, which fall within the scope of the present application. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
Claims (5)
1. An intelligent highway evaluation method is characterized by comprising the following steps:
Determining an evaluation object and a reference object, wherein the reference object comprises a first reference object and a second reference object, the first reference object is a common high-speed road section with equal traffic flow, similar weather conditions and vehicle type structures and the same lane number relative to the evaluation object, and the second reference object is a common high-speed road section before intelligent modification of the evaluation object;
Acquiring basic data for evaluation, wherein the basic data comprises construction engineering data, road operation data, operation data and satisfaction survey data;
An evaluation model is built, an evaluation index system of the evaluation model comprises three levels, namely a first level index, a second level index and a third level index, wherein the first level index comprises the intelligent sensing and service capability of an outfield, intelligent application, innovation application, development guarantee and comprehensive effect, each first level index comprises at least one second level index, each second level index comprises at least one third level index, and the indexes of each level respectively have corresponding weighting, scoring range and scoring rules of the third level index;
determining a comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and a third-level index score is obtained by basic data and a scoring rule;
Constructing an evaluation model, comprising:
Constructing an evaluation index system;
determining index weights by adopting expert scoring and a analytic hierarchy process;
The full score of the secondary index and the full score of the tertiary index are determined step by taking the primary index as a reference according to the index weight and the score of the corresponding previous index, and the score range of each tertiary index is 0 to full score;
Determining the index weight by combining expert scoring with analytic hierarchy process (ARP), comprising: determining index weights step by step according to the sequence of the layers from top to bottom, wherein the index weights for the same level comprise:
All indexes belonging to the same level of the same upper level index are ranked by m industry experts, the more important index ranking orders are more forward, the importance distances of two adjacent indexes are sequentially ranked, the importance distances are divided into a plurality of levels from high to low, the importance distances are respectively represented by numerical values, and the higher the importance distances are, the larger the corresponding numerical values are;
The importance of each index is determined by synthesizing the results given by m-bit experts, and the calculation formula is as follows:
Wherein:
b j represents the importance of the index j;
a ij represents the ranking order given by expert i for index j, if index j ranks to 1, a ij is 1;
s ij represents the importance difference rating between the index j given by the expert i and the index of the next rank, and the importance difference rating for the last index in the ranking is 0;
m represents expert number;
n represents the number of the same level index belonging to the same upper level index;
constructing a judgment matrix according to the importance scores of the indexes, and calculating formulas such as formula (2) and formula (3):
w ij represents the ratio of importance scores of the two indexes i and j, and is an element for constructing a judgment matrix, i and j epsilon (1-n);
n represents the number of the same level index belonging to the same upper level index;
calculating a consistency check index CR by using the formula (4) and the formula (5), if CR is smaller than a threshold value, the consistency passes the requirement,
Wherein:
lambda max represents the maximum eigenvalue of the judgment matrix;
n represents the number of the same level index belonging to the same upper level index;
The RI value is obtained by a consistency check table;
If the judgment matrix passes the consistency test, the feature vector (w 1,w2,…,wn)T represents the weight of each index, and the feature vector is normalized to obtain the weight value of each index;
obtaining a three-level index score from the base data and the scoring rules, comprising:
the method comprises the steps of designing three-level indexes subordinate to intelligent sensing and service capability, intelligent application, innovation application and development guarantee of an outfield into open indexes, determining initial scoring rules according to construction contents, determining initial scores of the three-level indexes according to the initial scoring rules, and marking the initial scores as technical compliance scores;
Correcting initial scores of three-level indexes of the intelligent sensing and service capability, the intelligent application and the innovation application of the outfield according to actual construction conditions, and marking the corrected scores as actual scores of the indexes;
setting three-level indexes subordinate to the comprehensive effect as objective indexes, and calculating actual scores according to basic statistical data;
the actual score of the three-level index of the intelligent sensing and service capability of the outfield is equal to the product of the initial score, the coverage degree coefficient and the reliability coefficient, and the actual score is calculated according to a formula (6):
Fk＝fk×αk×βk (6)
Wherein:
f k, the actual score of the kth three-level index;
f k -the technical compliance score of the kth three-level index, which is determined according to the technical compliance score rule;
Alpha k -the coverage degree coefficient of the kth three-level index, taking a value according to the proportion of highway mileage meeting the technical compliance requirement to the total mileage of the project, or the proportion of bridge/tunnel linear rice meeting the requirement to the total linear rice, or the proportion of the number of service areas meeting the requirement to the total number of the service areas, and taking a value of 1 when the value of the proportion is more than or equal to 80%; when the ratio is more than or equal to 50% and less than 80%, the coefficient is 0.7; when the ratio is less than 50%, the coefficient is 0.5;
Beta k, namely the reliability coefficient of the kth three-level index, taking a value according to the average online rate of various intelligent sensing and service facility equipment, wherein the average online rate is the ratio of the online time of the equipment which works normally to the total time, and the reliability coefficient is 1 when the average online rate is more than or equal to 90%; when the average online rate is more than or equal to 75% and less than 90%, the reliability coefficient is 0.7; when the average linear rate is less than 75%, the reliability coefficient is 0.5;
the actual score of the three-level index of the intelligent application is equal to the product of the technical compliance score and the system use degree coefficient, and the calculation is carried out according to a formula (7):
Fk＝fk×δk (7)
Wherein:
f k, the actual score of the kth three-level index;
f k -a technical compliance score for the kth tertiary index;
Delta k -a system use degree coefficient corresponding to the kth three-level index, taking 1 according to the liveness value of various system users, wherein the liveness is the proportion of the number of daily active users actually logging in and using the system to the number of users with opening permission, and the liveness is more than or equal to 80%; the activity is more than or equal to 50% and less than 80%, and the coefficient of the system use degree is 0.7; and when the activity is less than 50%, the system use degree coefficient is 0.5.
2. The method of claim 1, wherein the actual score of the tertiary index of the innovative application is comprehensively scored and modified based on the type, scale and public service experience of the innovative application, wherein each of the innovative applications not listed in the existing scoring rules and proven to be effective in practice can incorporate the innovative application index to calculate the score.
3. The method according to claim 1, wherein the three-level index subordinate to the comprehensive effect is an objective index, and the score is obtained by direct comparison calculation by selecting a reference object based on road running state data, operation data and satisfaction survey data.
4. The method of claim 1, wherein the composite score is calculated according to equation (8), equation (9) and equation (10):
Wherein:
F represents the comprehensive score of the intelligent expressway construction effect evaluation;
F i represents each first-level index score of the intelligent high-speed construction effect evaluation;
omega i represents the weight of the ith primary index F i, and the sum of the weights of all primary indexes is equal to 1;
f ij represents the actual score of the j-th secondary index set under the primary index F i;
f ijk represents the actual score of the kth third-level index set under the second-level index F ij;
x represents the number of all three-level indexes arranged under each two-level index;
y represents the number of all secondary indexes set under each primary index.
5. An intelligent highway evaluation apparatus for implementing the intelligent highway evaluation method according to claim 1, said apparatus comprising:
The system comprises a determining module, a judging module and a judging module, wherein the judging module is used for judging whether the evaluation object is an ordinary high-speed road section or not according to the vehicle type structure, the weather condition and the vehicle type structure of the evaluation object, and the lane number of the evaluation object is the same as that of the reference object;
The data module is used for acquiring basic data for evaluation, wherein the basic data comprises construction engineering data, road operation data, operation data and satisfaction survey data;
The computing module is used for constructing an evaluation model, wherein an evaluation index system of the evaluation model comprises three levels, namely a first-level index, a second-level index and a third-level index, the first-level index comprises the external intelligent sensing and service capability, intelligent application, innovative application, development guarantee and comprehensive effect, each first-level index comprises at least one second-level index, each second-level index comprises at least one third-level index, and the indexes of each level respectively have corresponding weighting, scoring range and scoring rules of the third-level index;
And determining a comprehensive score of the evaluation object according to the evaluation model, wherein the comprehensive score is obtained by a first-level index score, the score of each level of index is obtained by a corresponding next-level index score, and a third-level index score is obtained by basic data and a scoring rule.
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