CN114693072A - Motorcade structure analysis method, motorcade structure analysis system and storage medium - Google Patents
Motorcade structure analysis method, motorcade structure analysis system and storage medium Download PDFInfo
- Publication number
- CN114693072A CN114693072A CN202210210532.2A CN202210210532A CN114693072A CN 114693072 A CN114693072 A CN 114693072A CN 202210210532 A CN202210210532 A CN 202210210532A CN 114693072 A CN114693072 A CN 114693072A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- risk
- fleet
- score
- motorcade
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The application provides a motorcade structure analysis method, a motorcade structure analysis system and a computer medium, and particularly acquires vehicle information in a calculation cycle of a motorcade; the vehicle information comprises a license plate number, a license plate color and/or a frame number; inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle; according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule; and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation. According to all vehicles of the current fleet, the current maximum risk and the vehicle grade of each vehicle are analyzed by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional risk structure analysis of the motorcade are solved to a certain extent.
Description
Technical Field
The application belongs to the technical field of data analysis, and particularly relates to a motorcade structure analysis method, a motorcade structure analysis system and a storage medium.
Background
And the vehicle scoring system analyzes the risk structure of the motorcade vehicle, knows the possible risks of the personnel vehicle of the current motorcade in the operation and transportation process, has prospective judgment and provides decision basis for the subsequent operation decision.
However, most of the existing scoring analysis systems are directed to dimension analysis of a single vehicle, and complete analysis of vehicle factors such as driving behavior, vehicle operation time, vehicle operation speed, vehicle operation mileage and the like of the vehicle by using a single license plate number or frame number, so as to complete analysis of the operation condition of the whole fleet. However, for a medium-large fleet, the types of vehicles are various, the number of operating lines is large, the operating risk is high, the personnel and vehicle management cost is high, the actual operating conditions of the vehicles are difficult to grasp, and the like. In these cases, the operation of fleet services does not have a clear vehicle hierarchy description, and there is a significant scheduling risk when vehicle personnel adjustments and planning are made to the fleet.
Disclosure of Invention
The invention provides a motorcade structure analysis method, a motorcade structure analysis system and a storage medium, and aims to solve the problems of high analysis difficulty and complex analysis data in the conventional motorcade risk structure analysis.
According to a first aspect of the embodiments of the present application, there is provided a fleet structure analysis method, specifically including the following steps:
collecting vehicle information in a calculation cycle of a fleet; the vehicle information comprises a license plate number, a license plate color and/or a frame number;
inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle;
according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule;
and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
In some embodiments of the present application, inputting vehicle information into a vehicle risk evaluation model to obtain a risk score of each vehicle specifically includes:
determining a risk factor and a risk weight of the fleet according to vehicle information of the fleet;
and determining the risk score of each vehicle according to the risk factor and the risk weight.
In some embodiments of the present application, after inputting the vehicle information into the vehicle risk evaluation model and obtaining the risk score of each vehicle, the method further includes:
comparing and arranging the risk scores of all vehicles to obtain risk factors of which the risk scores of all vehicles are ranked in the top three;
integrating risk factors of the first three ranked risk scores of all vehicles to obtain a plurality of risk factors of the fleet;
and obtaining the score ratio of the risk factors of each fleet according to the risk score of each vehicle.
In some embodiments of the present application, after obtaining the score ratio of the risk factor of each fleet according to the risk score of each vehicle, the method further includes:
sorting according to the score ratio of each fleet risk factor in the fleet to obtain a fleet risk factor ranking;
and generating a comparison report according to the rank of the fleet risk factor, and displaying the comparison report.
In some embodiments of the present application, determining a risk score of each vehicle according to the risk factor and the risk weight specifically includes:
obtaining the risk value of each risk factor of the vehicle according to the risk factor value and the risk weight of the vehicle;
and adding the risk scores of all the risk factors of the vehicle to obtain the risk score of the vehicle.
In some embodiments of the present application, the mapping, according to the risk score of each vehicle and through a preset vehicle grade prediction rule, to obtain the vehicle grade of each vehicle and a corresponding vehicle evaluation specifically includes:
dividing the risk score into five numerical value ranges, wherein the five score ranges sequentially correspond to five grades of the vehicle;
according to the vehicle information data of the known vehicle grades, vehicle evaluation is respectively carried out on the five grades of the vehicle;
and correspondingly obtaining the vehicle grade and the vehicle evaluation corresponding to each vehicle according to the risk score of each vehicle.
In some embodiments of the present application, after generating a comparative report of the vehicle grades of the respective vehicles in the fleet and the corresponding vehicle evaluations, the method further includes:
adding the risk values of all vehicles in the fleet to obtain a total risk value of the current fleet;
acquiring a motorcade risk total value expected value, and comparing the motorcade risk total value with a current motorcade risk total value;
when the expected value of the risk total value of the fleet is larger than the current risk total value of the fleet, subtracting the vehicle with the largest risk score from the risk scores of all the vehicles; at the moment, the higher the risk score of the vehicle is, the higher the risk of the vehicle is;
when the expected value of the total risk value of the fleet is smaller than the current total risk value of the fleet, subtracting the vehicle with the minimum risk score from the risk scores of all the vehicles; the higher the risk score for a vehicle at that time, the lower the risk for that vehicle.
According to a second aspect of the embodiments of the present application, there is provided a fleet structure analysis system, specifically including:
the vehicle data acquisition module: the system is used for acquiring vehicle information in a calculation cycle of a motorcade; the vehicle information comprises license plate numbers, license plate colors and frame numbers;
a risk score calculation module: the system comprises a vehicle risk evaluation model, a vehicle risk evaluation model and a vehicle risk evaluation model, wherein the vehicle risk evaluation model is used for inputting vehicle information to the vehicle risk evaluation model to obtain risk scores of all vehicles;
a vehicle grade module: the system comprises a vehicle grade prediction module, a vehicle grade prediction module and a vehicle grade prediction module, wherein the vehicle grade prediction module is used for mapping to obtain the vehicle grade of each vehicle and corresponding vehicle evaluation according to the risk score of each vehicle;
the motorcade analysis module: and the system is used for generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
According to a third aspect of embodiments of the present application, there is provided a fleet structure analysis device, including:
a memory: for storing executable instructions; and
and the processor is connected with the memory to execute the executable instructions so as to complete the fleet structure analysis method.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement a fleet structure analysis method.
By adopting the motorcade structure analysis method, the motorcade structure analysis system and the computer medium in the embodiment of the application, the vehicle information in one calculation cycle of the motorcade is collected; the vehicle information comprises a license plate number, a license plate color and/or a frame number; inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle; according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule; and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation. According to all vehicles of the current fleet, the current maximum risk and the vehicle grade of each vehicle are analyzed by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional motorcade risk structure analysis are solved to a certain extent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
a schematic diagram of the steps of a fleet structure analysis method according to an embodiment of the present application is shown in fig. 1;
FIG. 2 is a schematic diagram illustrating steps for calculating a vehicle grade according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating steps for fleet structure adjustment according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating an implementation of the fleet structure analysis method according to the embodiment of the present application;
a fleet scoring structure presentation and risk factor analysis table according to an embodiment of the present application is shown in fig. 5;
a fleet expectation score evaluation table according to an embodiment of the application is shown in fig. 6;
a schematic structural diagram of a fleet structural analysis system according to an embodiment of the present application is shown in fig. 7;
a schematic structural diagram of a fleet structural analysis device according to an embodiment of the present application is shown in fig. 8.
Detailed Description
In the process of implementing the application, the inventor finds that most of the existing scoring analysis systems aim at the dimension analysis of a single vehicle, and completes the analysis of vehicle factors such as the driving behavior condition of the vehicle, the vehicle operation time period, the vehicle operation speed, the vehicle operation mileage and the like by using a single license plate number or frame number, thereby completing the analysis of the operation condition of the whole fleet. However, for medium and large-sized fleets, the types of vehicles are various, the number of operating lines is large, the operating risk is high, the personnel and vehicle management cost is high, and the actual operating conditions of the vehicles are difficult to grasp.
Based on the method, the current maximum risk and the vehicle grade of each vehicle are analyzed according to all vehicles of the current fleet by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional risk structure analysis of the motorcade are solved to a certain extent.
For the future development of the motorcade service, the vehicle type can be adjusted, calculation can be performed according to the current vehicle grade distribution condition of the motorcade, and the number of vehicles at each grade can be adjusted according to the rule to achieve the expected vehicle grade distribution.
Specifically, the application relates to a motorcade structure analysis method, a motorcade structure analysis system and a computer medium, and specifically, vehicle information in a calculation cycle of a motorcade is collected; the vehicle information comprises a license plate number, a license plate color and/or a frame number; inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle; according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule; and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
The innovation of the application is that:
analyzing and calculating the scoring structure proportion, the vehicle risk grade proportion, the operation condition proportion and the fleet operation condition of the current vehicle of the fleet; the expected motorcade risk scores are input for comparison, and the motorcade scores can be adjusted according to the current motorcade conditions to automatically measure and calculate the expected structure of the vehicle, so that the ideal vehicle service structure ratio is achieved.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
A schematic step diagram of a fleet structure analysis method according to an embodiment of the present application is shown in fig. 1.
As shown in fig. 1, the method for analyzing a fleet structure in an embodiment of the present application specifically includes the following steps:
s101: collecting vehicle information of a fleet in a calculation period; the vehicle information includes a license plate number, a license plate color, and/or a frame number.
The method is completed by adopting a foreground data acquisition and background real-time calculation return implementation mode.
First, vehicle basic information, such as license plate numbers and frame numbers, of all vehicles in a calculation period of a fleet of vehicles is obtained in batch, and the calculation period can be 3 months, 6 months or 12 months.
The specifically collected data further includes: vehicle attribute data, trip data, road data, and event data.
Wherein the vehicle attribute data includes vehicle identification codes, such as license plate numbers and frame numbers; but also the type of vehicle operation and affiliation to the fleet, etc.
The travel data includes state data of the vehicle during driving, such as driving time, driving mileage, direction angle, speed, acceleration, and the like.
The road data comprises dangerous intersections and dangerous road sections, the dangerous intersections comprise main road intersections and auxiliary road intersections, and the dangerous road sections comprise regions near cliffs and regions at high altitude.
The event data includes vehicle insurance data, maintenance data, violation data, weather data, and the like.
And finally, carrying out vehicle data deduplication and cleaning, and filtering out duplicate data or obvious error data. These data provide source data for vehicle assessments.
S102: and inputting the vehicle information to the vehicle risk evaluation model to obtain the risk score of each vehicle.
Firstly, model construction and training are needed, and a large amount of collected vehicle data are brought into the model for training. The vehicle risk evaluation principle of the vehicle risk evaluation model is as follows: determining risk factors and risk weights of the fleet according to a large amount of vehicle information; and then determining the risk score of each vehicle according to the risk factor and the risk weight.
Furthermore, after obtaining the vehicle information of the current fleet, the method specifically includes:
inputting vehicle information of a current fleet to a trained vehicle risk evaluation model, and determining risk factors and risk weights of the fleet by the vehicle risk evaluation model according to the vehicle information of the fleet; and finally, determining the risk score of each vehicle according to the risk factor and the risk weight.
The method for determining the risk score of each vehicle according to the risk factor and the risk weight specifically comprises the following steps: obtaining the risk value of each risk factor of the vehicle according to the risk factor value and the risk weight of the vehicle; and adding the risk scores of all the risk factors of the vehicle to obtain the risk score of the vehicle.
In the aspect of determining risk factors of a fleet, the risk factors are determined based on the current vehicles, and when an accident occurs due to risks, the risk factors are generally processed through an insurance platform. Therefore, the risk factors of the vehicle are in corresponding relation with the insurance data and the accident characteristics.
When the vehicle risk factors are evaluated, the vehicle data, the corresponding insurance data and the corresponding driving data are subjected to associated mapping, and then the K-means algorithm is used for performing cluster analysis according to the danger situation to summarize the operation accident characteristics to obtain K centroids, namely K risk factors.
Specifically, the clustered clusters of the vehicle data are divided into a plurality of sample data C1,C2,C3…Ck(ii) a The minimized square error is then denoted by E, and the calculation formula is:
wherein, muiIs a cluster CiThe mean vector of (a), also called centroid, i.e.:
the risk factors include driving mileage, highway proportion, night driving time, dangerous intersections, dangerous road sections, vehicles and operation types, vehicle violation conditions and the like when being implemented.
When this application embodiment is implemented specifically, what K centroids that obtain after through cluster analysis correspond is vehicle operation risk factor, include:
the first large factor: the method is determined by vehicle driving mileage, highway proportion and night driving duration, wherein the types of the highway comprise a common highway, a high-grade highway and a highway;
the second largest factor: the method is characterized by comprising the following steps of determining dangerous intersections, dangerous road sections, vehicles and operation types of vehicle driving paths, wherein the dangerous intersections comprise main road intersections and auxiliary road intersections, the dangerous road sections comprise regions near cliffs and high altitude regions, and the operation types comprise trunk line transportation, branch line vehicles and urban distribution;
the third major factor: the method is determined by a dangerous road section, a driving mileage and a night driving time of a vehicle driving path, wherein the dangerous road section comprises an area adjacent to a cliff and a high-altitude area;
other factors: the method is characterized by comprising the following steps of determining vehicle insurance times, maintenance times, insurance application time and money, violation times, fleet attributes and weather data, wherein the fleet attributes comprise vehicle scale, organization form and major business, and the major business is large, medium and small, and the weather data comprise humidity, temperature, visibility and wind level.
The risk weight of the risk factor is next determined.
Counting the occurrence frequency of each operation risk factor in an operation accident in a specified period, determining the weight occupied by each vehicle operation risk factor, and generating a weight mapping table, as shown in table 1:
table 1: the weight mapping table occupied by the risk factors is provided, wherein the weight WiThe calculation method is as follows:
wherein c is a risk factor,
indicating the total frequency of occurrence of operational accidents, FcIndicating the frequency of occurrence of the operational risk factor c in the total operational incident. Finally, obtaining the risk value of each risk factor of the vehicle according to the risk factor value and the risk weight of the vehicle; and adding the risk scores of all the risk factors of the vehicle to obtain the risk score of the vehicle.
Or according to the extracted risk factors of each operation, obtaining corresponding weight W by referring to a weight mapping table occupied by the risk factors in the table 1, and calculating the vehicle score through a weighted average value, wherein the calculation formula is as follows:
in the formula, n represents the total number of qualified operation risk factors, muiMean vector, w, representing the ith operational risk factoriAnd W represents a vehicle score obtained by carrying out weighted average on the operation risk factors meeting the conditions.
S103: and according to the risk score of each vehicle, mapping to obtain the vehicle grade of each vehicle and the corresponding vehicle evaluation through a preset vehicle grade prediction rule.
Wherein, when the vehicle grade divides the process: degree of similarity in the interior by aiIs shown as aiSmaller indicates that the sample i is clustered to the cluster; similarity between classes using biIs represented by biThe larger the sample i is, the less the sample i belongs to other clusters.
S for grade division standardiExpressed, the calculation formula is:
in the formula, SiIs an evaluation index for judging whether the vehicle grade division is reasonable, and the value is [ -1,1]The closer to 1, the more the sample i has the characteristics of the cluster; the closer to-1, the more the sample i does not have the sameThe characteristics of the cluster.
For example, vehicle information of 3 vehicles is selected, four groups of data are divided according to the vehicle operation risk factors to be subjected to weighted average calculation, the average score is 0.88, 0.71 and 0.58, and the higher the score is, the higher the vehicle risk probability is, and the lower the claim rate is.
A schematic diagram of the steps for calculating the vehicle grade according to an embodiment of the application is shown in fig. 2.
As shown in fig. 2, specifically, calculating the vehicle class includes the steps of:
s1031: dividing the risk score into five numerical value ranges, wherein the five value ranges sequentially correspond to five grades of the vehicle; other numbers of levels may be divided as desired.
S1032: according to the vehicle information data of the known vehicle grades, vehicle evaluation is respectively carried out on the five grades of the vehicle;
s1033: and correspondingly obtaining the vehicle grade and the vehicle evaluation corresponding to each vehicle according to the risk score of each vehicle.
S104: and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
In some embodiments of the present application, after generating a comparative report of the vehicle grades of the respective vehicles in the fleet and the corresponding vehicle evaluations, a fleet structure adjustment step is further included.
A schematic diagram of the steps of fleet structure adjustment according to an embodiment of the present application is shown in fig. 3.
As shown in fig. 3, the fleet structure adjustment specifically includes:
s1051: adding the risk values of all vehicles in the fleet to obtain a total risk value of the current fleet;
s1052: acquiring a motorcade risk total value expected value, and comparing the motorcade risk total value with a current motorcade risk total value;
s1053: when the risk total value expectation value of the fleet is greater than the current risk total value of the fleet, subtracting the vehicle with the highest risk score from the risk scores of all the vehicles; at the moment, the higher the risk score of the vehicle is, the higher the risk of the vehicle is;
when the expected value of the total risk value of the fleet is smaller than the current total risk value of the fleet, subtracting the vehicle with the minimum risk score from the risk scores of all the vehicles; the higher the risk score for a vehicle at that time, the lower the risk for that vehicle.
In other embodiments of the present application, after obtaining the risk score of each vehicle through S102, the method further includes:
comparing and arranging the risk scores of all vehicles to obtain risk factors of which the risk scores of all vehicles are ranked in the top three;
integrating risk factors of the risk scores of all vehicles ranked in the third place to obtain a plurality of risk factors of the fleet;
and obtaining the score ratio of the risk factors of each fleet according to the risk score of each vehicle.
Furthermore, after obtaining the score ratio of the risk factors of each fleet according to the risk score of each vehicle, the method further comprises the following steps: sorting according to the score ratio of each fleet risk factor in the fleet to obtain a fleet risk factor ranking; and generating a comparison report according to the rank of the fleet risk factor, and displaying the comparison report.
Fig. 4 is a schematic diagram illustrating an implementation principle of the fleet structure analysis method according to the embodiment of the present application.
As shown in fig. 4, the implementation of the fleet structure analysis and adjustment is as follows:
firstly, the vehicle information data is collected and processed,
and secondly, inquiring the vehicle related scores, namely determining the risk score of each vehicle according to the risk factors and the risk weight. At this time, the scoring rule needs to be preset in advance. And (4) bringing the vehicle score into a vehicle grade prediction table to obtain a vehicle evaluation conclusion, thereby helping the motorcade to optimize vehicle design.
The scoring rule is that: and according to the operation risk factors corresponding to the vehicles, comparing with a mapping table of the weight occupied by the risk factors in the table 1, combining the scores and the weights, and summing to calculate the risk score of each vehicle. Referring again to fig. 5, a score is calculated for each vehicle.
Fig. 5 illustrates a fleet scoring structure presentation and risk factor analysis table according to an embodiment of the present application.
As shown in fig. 5, specifically, vehicle operation information of a fleet is collected and subjected to weight removal, evaluation calculation is performed on the vehicle subjected to weight removal, the number of evaluated vehicles, the ratio of the evaluated vehicles, the average score and the expected base cost odds corresponding to each grade are obtained, and the maximum operation risk factor of the vehicle is judged and obtained through risk factor analysis on the evaluated vehicles. And finally, combining the risk factors of the first three ranked vehicles, performing specific gravity sorting on the types of the risk factors, and combining to form the risk factor proportion condition of the fleet.
When ranking each vehicle risk factor actually, the following is implemented: assume that the current fleet has 5 vehicles A, B, C, D, E.
Vehicle a front three major risk factors: night driving, mileage, vehicle violation.
Three major risk factors in front of vehicle B: night driving, mileage, vehicle violation.
Vehicle C front three major risk factors: mileage, dangerous road sections and vehicle violation conditions.
Three major risk factors in front of vehicle D: night driving, mileage, vehicle violation.
Three major risk factors in front of vehicle E: vehicle and operation type, mileage and vehicle violation condition.
And then the maximum risk factors of each vehicle are judged to be night driving, driving mileage, vehicles and operation types respectively.
Then, the number of vehicles of each risk factor is calculated: night driving mileage 3 vehicles, driving mileage 1 vehicle, vehicle and operation type 1 vehicle.
Calculating the proportion of each risk factor in all current risk factors:
the proportion of night driving is as follows: 3/5 ═ 60%; the proportion of the driving mileage is as follows: 1/5 ═ 20%; the proportion of the vehicles and the operation types is as follows: 1/5 ═ 20%; sorting the proportion of the first large factors of all the current vehicles, and taking the factor with the largest proportion as the largest risk factor of the current fleet: 60% > 20% > 20%, so night driving is the first major risk factor of the current fleet.
Similarly, the calculation method of the second major risk factor of the fleet and the third major risk factor of the fleet are the same as the above.
And further obtaining risk factors of top three of each vehicle ranking in the fleet.
And then, carrying out statistical ranking according to the scores of the vehicle risk factors, displaying the structural data of the fleet score, and displaying the calculated vehicle score grade in a table form. Alternatively, the vehicle risk behavior is displayed in a list.
Fig. 6 shows a fleet expectation score evaluation table according to an embodiment of the application, and after calculating and displaying the risk scores of the individual vehicles of the fleet, evaluation conclusions of the fleet are obtained according to the scores.
As shown in fig. 6, specifically, according to the score of each vehicle of a fleet, a target average score of the fleet is calculated, and according to the score level corresponding to the target average score, an evaluation conclusion of the fleet is obtained: i.e., the number of vehicles suggested, the percentage of vehicles suggested, and the expected pay-per-view rate. The non-scored vehicles are displayed to a non-scoring scale. The motorcade structure is specifically adjusted as follows:
1) after the vehicle scoring structure assessment is completed, an expected average score for the fleet, i.e., a total fleet risk value expectation, of no more than 100 points is entered at the interface or page.
2) And circularly subtracting the score of one vehicle from the minimum score interval until the lowest average score of the vehicle is larger than the expected value range of the total risk value of the fleet, and returning the result of the vehicle score structure. In this case, the higher the risk score of the vehicle, the lower the risk of the vehicle, and the current total risk value of the vehicle needs to be greater than the expected value of the total risk value of the fleet.
The following specific embodiments are described:
assume that there are currently A, B, C, D, E, F, G7 vehicles:
vehicle a scoring: 92; vehicle B scoring: 75; vehicle C scoring: 60; vehicle D scoring: 55; vehicle E scoring: 20; and (3) vehicle F scoring: 68; vehicle G scoring: 17.
first, a user-expected fleet average score 50 is obtained. The total risk value of the fleet expected by the user can be input, and the judgment and calculation process is similar, and the expected average of the fleet is taken as an example for explanation.
Calculating the average score of all current vehicles: is 55.28571. The specific calculation process is as follows: 92+75+60+55+30+68+17 387; 387/7 ═ 55.28571.
And then, judging whether the minimum score of the current vehicle is greater than 50, if so, finishing the judgment and returning the current vehicle to the user. And if the minimum score of the vehicle is not more than 50, performing the next judgment.
Judging whether the average score of the current ABCDEFG vehicle meets the condition, and if so, returning the average score to the current vehicle of the user; if the condition is not met, the next operation is carried out: the vehicle G with the lowest score is subtracted from all the vehicles to calculate the vehicle average score 61.66666, and the specific calculation process is 92+75+60+55+20+ 68-370; 370/6 ═ 61.66666. And circulating the operation until the minimum score of the current vehicle is more than 50.
The fleet structure analysis method in the embodiment of the application specifically acquires vehicle information in a calculation cycle of a fleet; the vehicle information comprises a license plate number, a license plate color and/or a frame number; inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle; according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule; and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation. According to all vehicles of the current fleet, the current maximum risk and the vehicle grade of each vehicle are analyzed by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional risk structure analysis of the motorcade are solved to a certain extent.
Example 2
For details not disclosed in the fleet structure analysis system of this embodiment, please refer to specific implementation contents of the fleet structure analysis method in other embodiments.
A schematic structural diagram of a fleet structural analysis system according to an embodiment of the present application is shown in fig. 7.
As shown in fig. 7, the fleet structure analysis system according to the embodiment of the present disclosure specifically includes a vehicle data collection module 10, a risk score calculation module 20, a vehicle class module 30, and a fleet analysis module 40.
In particular, the method comprises the following steps of,
the vehicle data acquisition module 10: the system is used for acquiring vehicle information in a calculation cycle of a motorcade; the vehicle information includes a license plate number, a license plate color, and a frame number.
First, vehicle basic information, such as license plate numbers and frame numbers, of all vehicles in a calculation period of a fleet of vehicles is obtained in batches, and the calculation period can be 3 months, 6 months or 12 months.
And then, carrying out vehicle data deduplication and cleaning, and filtering out duplicate data or obvious error data. These data provide source data for vehicle assessments.
Risk score calculation module 20: and the risk evaluation module is used for inputting the vehicle information to the vehicle risk evaluation model to obtain the risk score of each vehicle.
Firstly, model construction and training are needed, and a large amount of collected vehicle data are brought into the model for training. The vehicle risk evaluation principle of the vehicle risk evaluation model is as follows: determining risk factors and risk weights of the fleet according to a large amount of vehicle information; and then determining the risk score of each vehicle according to the risk factor and the risk weight.
Furthermore, after obtaining the vehicle information of the current fleet, the method specifically includes:
inputting vehicle information of a current fleet to a trained vehicle risk evaluation model, and determining risk factors and risk weights of the fleet by the vehicle risk evaluation model according to the vehicle information of the fleet; and finally, determining the risk score of each vehicle according to the risk factor and the risk weight.
Wherein, according to the risk factor and the risk weight, determining the risk score of each vehicle specifically comprises: obtaining the risk value of each risk factor of the vehicle according to the risk factor value and the risk weight of the vehicle; and adding the risk scores of all the risk factors of the vehicle to obtain the risk score of the vehicle.
The vehicle class module 30: and the vehicle grade prediction method is used for mapping to obtain the vehicle grade of each vehicle and the corresponding vehicle evaluation according to the risk score of each vehicle and a preset vehicle grade prediction rule.
Specifically, calculating the vehicle grade includes:
dividing the risk score into five numerical value ranges, wherein the five score ranges sequentially correspond to five grades of the vehicle;
according to the vehicle information data of the known vehicle grades, vehicle evaluation is respectively carried out on the five grades of the vehicle;
and correspondingly obtaining the vehicle grade and the vehicle evaluation corresponding to each vehicle according to the risk score of each vehicle.
Fleet analysis module 40: and the system is used for generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
In some embodiments of the present application, after generating a comparative report of the vehicle grades of the respective vehicles in the fleet and the corresponding vehicle evaluations, a fleet structure adjustment module is further included.
The fleet structure adjustment module specifically includes:
adding the risk values of all vehicles in the fleet to obtain a total risk value of the current fleet;
acquiring a motorcade risk total value expected value, and comparing the motorcade risk total value with a current motorcade risk total value;
when the expected value of the risk total value of the fleet is larger than the current risk total value of the fleet, subtracting the vehicle with the largest risk score from the risk scores of all the vehicles; at the moment, the higher the risk score of the vehicle is, the higher the risk of the vehicle is; when the expected value of the total risk value of the fleet is smaller than the current total risk value of the fleet, subtracting the vehicle with the minimum risk score from the risk scores of all the vehicles; the higher the risk score for a vehicle at that time, the lower the risk for that vehicle.
In the fleet structure analysis system in the embodiment of the application, specifically, the vehicle data acquisition module 10 acquires vehicle information in a calculation cycle of a fleet; the vehicle information comprises a license plate number, a license plate color and/or a frame number; the risk score calculation module 20 inputs vehicle information to the vehicle risk evaluation model to obtain the risk score of each vehicle; the vehicle grade module 30 maps the vehicle grade of each vehicle and the corresponding vehicle evaluation according to the risk score of each vehicle and a preset vehicle grade prediction rule; the fleet analysis module 40 generates and displays a comparison report of the vehicle grades of each vehicle in the fleet and the corresponding vehicle evaluations. According to all vehicles of the current fleet, the current maximum risk and the vehicle grade of each vehicle are analyzed by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional risk structure analysis of the motorcade are solved to a certain extent.
Example 3
For details not disclosed in the fleet structure analysis device of this embodiment, please refer to specific implementation contents of the fleet structure analysis method or system in other embodiments.
A schematic structural diagram of a fleet structure analysis device 400 according to an embodiment of the present application is shown in fig. 8.
As shown in fig. 8, the fleet structure analysis device 400 includes:
the memory 402: for storing executable instructions; and
a processor 401 is coupled to the memory 402 to execute executable instructions to perform the motion vector prediction method.
It will be understood by those skilled in the art that the schematic diagram 8 is merely an example of the fleet structure analysis device 400 and does not constitute a limitation of the fleet structure analysis device 400, and may include more or fewer components than shown, or combine certain components, or different components, e.g., the fleet structure analysis device 400 may also include input output devices, network access devices, buses, etc.
The Processor 401 (CPU) may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor 401 may be any conventional processor or the like, and the processor 401 is the control center for the fleet structure analysis device 400 and connects the various components of the entire fleet structure analysis device 400 using various interfaces and lines.
The memory 402 may be used to store computer readable instructions and the processor 401 may implement the various functions of the fleet structure analysis device 400 by executing or executing computer readable instructions or modules stored in the memory 402 and invoking data stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the fleet structure analysis device 400, and the like. In addition, the Memory 402 may include a hard disk, a Memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Memory Card (Flash Card), at least one disk storage device, a Flash Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), or other non-volatile/volatile storage devices.
The modules integrated by the fleet structure analysis device 400 may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by hardware related to computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program; the computer program is executed by a processor to implement the fleet structure analysis method in other embodiments.
The motorcade structure analysis equipment and the computer storage medium in the embodiment of the application specifically acquire vehicle information in a calculation cycle of a motorcade; the vehicle information comprises a license plate number, a license plate color and/or a frame number; inputting vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle; according to the risk score of each vehicle, the vehicle grade of each vehicle and the corresponding vehicle evaluation are obtained through mapping according to a preset vehicle grade prediction rule; and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation. According to all vehicles of the current fleet, the current maximum risk and the vehicle grade of each vehicle are analyzed by combining a unified vehicle risk evaluation model, and finally, a proportion report is formed. The problems of high analysis difficulty and complex analysis data in the conventional risk structure analysis of the motorcade are solved to a certain extent.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this disclosure and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A fleet structure analysis method, comprising:
collecting vehicle information of a fleet in a calculation period;
inputting the vehicle information to a vehicle risk evaluation model to obtain a risk score of each vehicle;
according to the risk score of each vehicle, mapping to obtain the vehicle grade of each vehicle and corresponding vehicle evaluation through a preset vehicle grade prediction rule;
and generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
2. The fleet structure analysis method according to claim 1, wherein said inputting said vehicle information to a vehicle risk assessment model to obtain a risk score of each vehicle comprises:
determining a risk factor and a risk weight of the fleet according to vehicle information of the fleet;
and determining the risk score of each vehicle according to the risk factor and the risk weight.
3. The fleet structure analysis method according to claim 2, wherein said inputting said vehicle information into a vehicle risk assessment model, after obtaining a risk score of each vehicle, further comprises:
comparing and arranging the risk scores of all vehicles to obtain risk factors of which the risk scores of all vehicles are ranked in the top three;
integrating risk factors of the risk scores of all vehicles ranked in the third place to obtain a plurality of risk factors of the fleet;
and obtaining the score ratio of the risk factors of each fleet according to the risk score of each vehicle.
4. The method for analyzing the structure of the fleet of claim 3, wherein said obtaining the score ratio of the risk factors of each fleet according to the risk scores of each vehicle further comprises:
sorting according to the score ratio of each fleet risk factor in the fleet to obtain a fleet risk factor ranking;
and generating a comparison report according to the rank of the fleet risk factor, and displaying the comparison report.
5. The fleet structure analysis method according to claim 2, wherein said determining a risk score of each vehicle according to said risk factor and said risk weight comprises:
obtaining the risk value of each risk factor of the vehicle according to the risk factor value and the risk weight of the vehicle;
and adding the risk scores of all the risk factors of the vehicle to obtain the risk score of the vehicle.
6. The fleet structure analysis method according to claim 1, wherein the mapping of the vehicle grades of each vehicle and the corresponding vehicle evaluations according to the risk scores of each vehicle and preset vehicle grade prediction rules comprises:
equally dividing the risk score into five numerical value ranges, wherein the five score ranges sequentially correspond to five grades of the vehicle;
according to vehicle information data of known vehicle grades, vehicle evaluation is respectively carried out on the five grades of the vehicle;
and correspondingly obtaining the vehicle grade and the vehicle evaluation corresponding to each vehicle according to the risk score of each vehicle.
7. The method for analyzing the fleet structure according to claim 1, wherein after generating the comparative report of the vehicle grades and corresponding vehicle evaluations of each vehicle in the fleet, the method further comprises:
adding the risk values of all vehicles in the fleet to obtain a total risk value of the current fleet;
acquiring a motorcade risk total value expected value, and comparing the motorcade risk total value with a current motorcade risk total value;
when the total risk value expectation of the fleet is greater than the current total risk value of the fleet, subtracting the vehicle with the highest risk value in the risk values of all the vehicles;
and when the expected value of the total risk value of the fleet is smaller than the current total risk value of the fleet, subtracting the vehicle with the minimum risk score from the risk scores of all the vehicles.
8. A fleet structure analysis system, specifically comprising:
the vehicle data acquisition module: the system is used for acquiring vehicle information in a calculation cycle of a motorcade; a risk score calculation module: the system is used for inputting the vehicle information to a vehicle risk evaluation model to obtain the risk score of each vehicle;
a vehicle grade module: the system comprises a vehicle grade prediction module, a vehicle grade prediction module and a vehicle grade prediction module, wherein the vehicle grade prediction module is used for mapping to obtain the vehicle grade of each vehicle and corresponding vehicle evaluation according to the risk score of each vehicle;
the motorcade analysis module: and the system is used for generating and displaying a comparison report of the vehicle grade of each vehicle in the fleet and the corresponding vehicle evaluation.
9. A fleet structure analysis device, comprising:
a memory for storing executable instructions; and
a processor coupled to the memory for executing the executable instructions to perform the fleet structure analysis method of any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program; a computer program for execution by a processor for implementing a fleet structure analysis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210210532.2A CN114693072A (en) | 2022-03-03 | 2022-03-03 | Motorcade structure analysis method, motorcade structure analysis system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210210532.2A CN114693072A (en) | 2022-03-03 | 2022-03-03 | Motorcade structure analysis method, motorcade structure analysis system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114693072A true CN114693072A (en) | 2022-07-01 |
Family
ID=82137763
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210210532.2A Pending CN114693072A (en) | 2022-03-03 | 2022-03-03 | Motorcade structure analysis method, motorcade structure analysis system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114693072A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117238124A (en) * | 2023-06-20 | 2023-12-15 | 深圳民太安智能科技有限公司 | Multi-dimensional risk factor-based vehicle safe driving grading early warning method and system |
-
2022
- 2022-03-03 CN CN202210210532.2A patent/CN114693072A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117238124A (en) * | 2023-06-20 | 2023-12-15 | 深圳民太安智能科技有限公司 | Multi-dimensional risk factor-based vehicle safe driving grading early warning method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108230676A (en) | A kind of intersection pedestrian's street crossing methods of risk assessment based on track data | |
| CN106127586A (en) | Vehicle insurance rate aid decision-making system under big data age | |
| Kruber et al. | Unsupervised and supervised learning with the random forest algorithm for traffic scenario clustering and classification | |
| CN108764375B (en) | Highway goods stock transprovincially matching process and device | |
| CN105809193B (en) | A kind of recognition methods of the illegal vehicle in use based on kmeans algorithm | |
| WO2020108219A1 (en) | Traffic safety risk based group division and difference analysis method and system | |
| CN112116263B (en) | Traffic intersection risk level assessment method and device, electronic equipment and storage medium | |
| Xing et al. | Time-varying analysis of traffic conflicts at the upstream approach of toll plaza | |
| CN111105628A (en) | Parking lot portrait construction method and device | |
| CN109784586B (en) | Prediction method and system for danger emergence condition of vehicle danger | |
| CN108573600B (en) | Driver behavior induction and local traffic flow optimization method | |
| CN115422747A (en) | Method and device for calculating discharge amount of pollutants in tail gas of motor vehicle | |
| CN109791677A (en) | System and method for carrying out Geographic Reference and scoring to vehicle data in community | |
| CN112150046A (en) | Road intersection safety risk index calculation method | |
| CN114693072A (en) | Motorcade structure analysis method, motorcade structure analysis system and storage medium | |
| WO2017221856A1 (en) | Analysis device, analysis method, and recording medium | |
| CN117787796A (en) | Evaluation method, device and equipment for automatic driving test | |
| CN112308136A (en) | SVM-Adaboost-based driving distraction detection method | |
| CN116798223A (en) | Sub-region division and state identification method based on macroscopic basic diagram/FCM clustering | |
| CN114066288B (en) | Intelligent data center-based emergency detection method and system for operation road | |
| Cen et al. | A system design for driving behavior analysis and assessment | |
| CN113191805B (en) | Vehicle owner replacement evaluation method, system, electronic equipment and storage medium | |
| CN112766567B (en) | Evaluation method, system and storage medium for urban road network planning implementation effect | |
| CN113192340B (en) | Method, device, equipment and storage medium for identifying highway construction vehicles | |
| Bäumler et al. | Report on validation of the stochastic traffic simulation (Part B) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |