CN114783169A - Fleet management method and device, electronic equipment and storage medium - Google Patents
Fleet management method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of fleet management, in particular to a fleet management method, a fleet management device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring target characteristics corresponding to a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet; weighting each target feature to generate each weighted feature; generating a first feature corresponding to the first level and a second feature corresponding to the second level based on the weighted features; and determining a management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result. The fleet management method considers the management of vehicles and drivers in the fleet, and also considers the management of driving behaviors and safety management, so that the target fleet can be comprehensively and efficiently managed.
Description
Technical Field
The invention relates to the field of fleet management, in particular to a fleet management method and device, electronic equipment and a storage medium.
Background
With the continuous iterative development of the car networking technology, more refined management of the heavy locomotive fleet becomes possible.
The traditional fleet management is basically managed by subjective images of managers, and no objective data is used for supporting and evaluating, so that oil theft, material stealing, abnormal unloading, abnormal running tracks and other things which damage the benefits of companies in the heavy-duty machine industry are difficult to completely eradicate, the possibility of vehicle accidents is possibly greatly increased due to the nonstandard running of drivers, and if the situations cannot be timely and effectively avoided, the operation of the vehicles can face huge risks, so that a scientific management scoring method based on the existing vehicle networking technology becomes valuable. In the future, more scientific and efficient fleet management has become a trend, and is the key to determining whether a fleet can successfully operate for profit.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a fleet management method, which aims to solve the problem of how to efficiently manage a fleet.
According to a first aspect, an embodiment of the present invention provides a fleet management method, including:
acquiring target characteristics corresponding to a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet;
carrying out weighting processing on each target characteristic to generate each weighted characteristic;
generating a first feature corresponding to the first level and a second feature corresponding to the second level based on the weighted features;
and determining a management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
The motorcade management method provided by the embodiment of the invention obtains the corresponding target characteristics of the target motorcade in the target time period; the target characteristics include vehicle target characteristics, driver target characteristics, driving behavior target characteristics, and safety target characteristics in a target fleet. And each target feature is subjected to weighting processing to generate each weighting feature, so that the accuracy of the generated weighting features is ensured. Based on the weighted features, a first feature corresponding to the first level and a second feature corresponding to the second level are generated, so that the accuracy of the generated first feature corresponding to the first level and the generated second feature corresponding to the second level is guaranteed. And then, based on the relation between the target characteristic and the first characteristic and the second characteristic, the management evaluation result of the target fleet is determined, so that the accuracy of the determined management evaluation result is ensured. And then, a management scheme corresponding to the target fleet is generated based on the management evaluation result, so that the accuracy of the generated management scheme corresponding to the target fleet is ensured. The fleet management method takes the management of the vehicles and the drivers in the fleet into consideration, and also takes the management of the driving behaviors and the safety management into consideration, so that the finally generated management scheme corresponding to the target fleet can realize the management of the vehicles and the drivers in the target fleet, and also can realize the management of the driving behaviors and the safety management in the target fleet, thereby realizing the comprehensive and efficient management of the target fleet.
With reference to the first aspect, in a first implementation manner of the first aspect, generating a first feature corresponding to a first level and a second feature corresponding to a second level based on each of the weighted features includes:
determining each weighted feature with the maximum value from each weighted feature based on the value of each weighted feature corresponding to each time point to generate a first feature;
based on the values of the weighted features corresponding to the time points, the weighted features with the smallest values are determined from the weighted features, and the second feature is generated.
According to the fleet management method provided by the embodiment of the invention, each weighting characteristic with the maximum value is determined from each weighting characteristic based on the value of each weighting characteristic corresponding to each time point, so that the first characteristic is generated, and the accuracy of the generated first characteristic is ensured. Then, based on the values of the weighted features corresponding to the respective time points, each weighted feature having the smallest value is determined from the weighted features, and the second feature is generated. The accuracy of the generated second feature is guaranteed.
With reference to the first aspect, in a second implementation manner of the first aspect, determining a management evaluation result of the target fleet based on a relationship between the target feature and the first feature and the second feature includes:
calculating a first distance between the first feature and the target feature based on the first feature and the target feature;
calculating a second distance between the second feature and the target feature based on the second feature and the target feature;
and determining a management evaluation result of the target fleet based on the first distance and the second distance.
According to the fleet management method provided by the embodiment of the invention, the first distance between the first characteristic and the target characteristic is calculated based on the first characteristic and the target characteristic, so that the accuracy of the calculated first distance between the first characteristic and the target characteristic is ensured. And calculating a second distance between the second feature and the target feature based on the second feature and the target feature, so that the accuracy of the calculated second distance between the second feature and the target feature is ensured. And then, the management evaluation result of the target motorcade is determined based on the first distance and the second distance, so that the accuracy of the determined management evaluation result of the target motorcade is ensured.
With reference to the second implementation manner of the first aspect, in the third implementation manner of the first aspect, the determining the management evaluation result of the target fleet based on the first distance and the second distance includes
And dividing the first distance by the sum of the first distance and the second distance to obtain a management evaluation result of the target motorcade.
According to the fleet management method provided by the embodiment of the invention, the first distance is divided by the sum of the first distance and the second distance to obtain the management evaluation result of the target fleet, so that the accuracy of the calculated management evaluation result of the target fleet is ensured.
With reference to the first aspect, in a fourth implementation manner of the first aspect, performing weighting processing on each target feature to generate each weighted feature includes:
calculating information entropy corresponding to each time point of each target feature in a target time period based on the relation between each target feature;
calculating the weight information of each target feature corresponding to each time point based on the relation between each information entropy and each target feature;
each target feature is multiplied by the weight information to generate each weighted feature.
According to the fleet management method provided by the embodiment of the invention, the information entropy corresponding to each time point of each target feature in the target time period is calculated based on the relationship among the target features, so that the accuracy of the calculated information entropy corresponding to each time point of each target feature is ensured. Then, based on the relation between each information entropy and each target feature, the weight information of each target feature corresponding to each time point is calculated, and the accuracy of the calculated weight information of each target feature corresponding to each time point is ensured. And finally, multiplying each target feature by the weight information to generate each weighted feature, thereby ensuring the accuracy of each generated weighted feature.
With reference to the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the calculating weight information of each target feature at each time point based on a relationship between each information entropy and each target feature includes:
subtracting the information entropy corresponding to each time point by using the target parameter to obtain the target information entropy corresponding to each time point;
and dividing the target information entropy by the sum of the target information entropies to obtain the weight information of each target feature corresponding to each time point.
According to the fleet management method provided by the embodiment of the invention, the target information entropy corresponding to each time point is obtained by subtracting the information entropy corresponding to each time point from the target parameter, so that the accuracy of the target information entropy corresponding to each time point obtained through calculation is ensured. Then, based on each target information entropy, dividing the sum of each target information entropy to obtain the weight information of each target feature corresponding to each time point, so that the accuracy of the obtained weight information of each target feature corresponding to each time point is ensured, and the accuracy of each generated weight feature is further ensured.
With reference to the first aspect, in a sixth implementation manner of the first aspect, the obtaining a target feature corresponding to a target fleet in a target time period includes:
acquiring corresponding basic features of a target fleet in a target time period;
and carrying out normalization processing on each basic feature to generate each target feature corresponding to each basic feature.
According to the fleet management method provided by the embodiment of the invention, the corresponding basic characteristics of the target fleet in the target time period are obtained, then the normalization processing is carried out on the basic characteristics, and the target characteristics corresponding to the basic characteristics are generated, so that the accuracy of the target characteristics corresponding to the generated basic characteristics is ensured.
According to a second aspect, an embodiment of the present invention further provides a fleet management device, including:
the acquisition module is used for acquiring corresponding target characteristics of a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet;
the first generation module is used for carrying out weighting processing on each target characteristic to generate each weighted characteristic;
the second generation module is used for generating a first feature corresponding to the first grade and a second feature corresponding to the second grade based on the weighted features;
and the determining module is used for determining the management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
The motorcade management device provided by the embodiment of the invention acquires the corresponding target characteristics of a target motorcade in a target time period; the target characteristics include vehicle target characteristics, driver target characteristics, driving behavior target characteristics, and safety target characteristics in a target fleet. And each target feature is subjected to weighting processing to generate each weighting feature, so that the accuracy of the generated weighting features is ensured. Based on the weighted features, a first feature corresponding to the first level and a second feature corresponding to the second level are generated, so that the accuracy of the generated first feature corresponding to the first level and the generated second feature corresponding to the second level is guaranteed. Then, the management evaluation result of the target fleet is determined based on the relation between the target characteristic and the first characteristic and the second characteristic, and the accuracy of the determined management evaluation result is guaranteed. And then, based on the management evaluation result, a management scheme corresponding to the target fleet is generated, so that the accuracy of the generated management scheme corresponding to the target fleet is ensured. The fleet management method takes the management of the vehicles and the drivers in the fleet as well as the management of the driving behaviors and the safety management into consideration, so that the finally generated management scheme corresponding to the target fleet can realize the management of the vehicles and the drivers in the target fleet and also realize the management of the driving behaviors and the safety management in the target fleet, thereby realizing the comprehensive and efficient management of the target fleet.
According to a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device is a cloud server or/and a vehicle controller, and the electronic device includes a memory and a processor, where the memory and the processor are communicatively connected to each other, and a computer instruction is stored in the memory, and the processor executes the computer instruction, so as to execute the fleet management method in the first aspect or any one of the implementations of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method for fleet management in the first aspect or any one of the embodiments of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a fleet management method provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a fleet management method provided by another embodiment of the present invention;
FIG. 3 is a flow diagram of a fleet management method provided by another embodiment of the present invention;
FIG. 4 is a flow diagram of a fleet management method provided by another embodiment of the present invention;
FIG. 5 is a flow chart of a fleet management method provided by another embodiment of the present invention;
FIG. 6 is a functional block diagram of a fleet management device provided by embodiments of the present invention;
fig. 7 is a schematic diagram of a hardware structure of an electronic device to which an embodiment of the present invention is applied.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It should be noted that, in the method for fleet management provided in the embodiment of the present application, an execution subject of the method may be a fleet management apparatus, and the fleet management apparatus may be implemented as part or all of an electronic device in a software, hardware, or a combination of software and hardware, where the electronic device may be a server or a terminal, and may also be a controller on a piloted vehicle that is traveling in a formation. The server in the embodiment of the present application may be one server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application may be another intelligent hardware device such as a smart phone, a personal computer, a tablet computer, a wearable device, and an intelligent robot. In the following method embodiments, the execution subject is an electronic device as an example.
In an embodiment of the present application, as shown in fig. 1, a fleet management method is provided, which is described by taking an example of the method applied to an electronic device, and includes the following steps:
and S11, acquiring the corresponding target characteristics of the target fleet in the target time period.
The target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet.
The target features of the driver can include age information of the driver, years of work, records of work (such as violation records, accident records and the like) and physical health conditions, and the like, and the overall level of the driver can be measured through the target features of the driver. The vehicle target characteristics can include whether the vehicle is maintained on time, whether vehicle sanitation is cleaned according to regulations, whether vehicle-mounted tools are complete, whether vehicle files are maintained normally, whether a worker works on time on a working day and the like. The driving behavior target characteristics may include whether the driver is driving fatigue, the number of rapid accelerations, the number of rapid decelerations, the number of rapid turns, the fuel consumption of one hundred kilometers, and the like. The safety target characteristics can comprise the times of red light running of a driver, the times of electronic fence exit, the times of abnormal oil consumption, the times of oil stealing and material stealing, the times of abnormal material unloading and the like, and whether the safety consciousness of the driver is weak or not can be measured through the safety target characteristics.
Wherein the target fleet may be a transportation fleet.
Specifically, the electronic equipment can receive target characteristics, corresponding to a target fleet in a target time period, input by a user; the electronic device can also receive the target characteristics of the target fleet transmitted by other devices in the target time period. The implementation of the application does not specifically limit the way in which the electronic device acquires the target characteristics of the target fleet in the target time period.
The target time period may be several months of time, for example, 1 month, 2 months, and 3 months, the target time period may be several days of time, for example, 1 st to 15 th of a certain month, and the target time period is not specifically limited in the embodiment of the present application.
Details regarding this step will be described below.
S12, the target features are weighted to generate weighted features.
In an optional implementation manner of the present application, the electronic device may obtain priorities corresponding to each target feature, determine weight information corresponding to each target feature according to the priorities corresponding to each target feature, and then multiply each target feature by the weight information to generate a weighted feature.
Details regarding this step will be described below.
S13, based on the weighted features, a first feature corresponding to the first rank and a second feature corresponding to the second rank are generated.
Wherein the first level is higher than the second level and the first characteristic is better than the second characteristic.
In an alternative embodiment, the first feature may characterize an optimal feature of the weighted features, and the first feature may characterize a worst feature of the weighted features.
In an optional embodiment of the present application, the electronic device may generate a first feature corresponding to the first level and a second feature corresponding to the second level according to the superiority and inferiority of each weighting feature.
Details regarding this step will be described below.
And S14, determining a management evaluation result of the target fleet based on the relationship between the target characteristics and the first characteristics and the second characteristics, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
In an optional implementation manner of the present application, the electronic device may compare the target feature with the first feature and the second feature, respectively, and determine a relationship between the target feature and the first feature and the second feature according to a comparison result. And then determining the management evaluation result of the target fleet according to the relationship between the target characteristic and the first characteristic and the second characteristic.
The management evaluation result of the target vehicle group may be a score corresponding to the target vehicle group, for example, 90 points, 85 points, or the like, or a rank corresponding to the target vehicle group, for example, A, B, C, D, or the like, or the first, second, third, or the like, or may be excellent, good, qualified, unqualified, or the like, and the management evaluation result of the target vehicle group is not specifically limited in the embodiment of the present application.
In an optional embodiment of the present application, the electronic device may further determine a ranking of the target fleet according to a management evaluation result of the target fleet. E.g., rank first, second, third, etc.
For example, the better the management evaluation result is, the better the management level of the target vehicle fleet is, and the smaller the management evaluation result is, the worse the management level of the target vehicle fleet is. When the management evaluation result of the target fleet is the corresponding score of the target fleet, the section (0,60) is classified as 'to be improved'; the final score was classified as "pass" in the interval [60, 74); the final score was classified as "good" in the interval [74, 84); the final score was classified as "excellent" in the interval [84, 100).
After determining the management evaluation result of the target fleet, the electronic device may generate a management scheme corresponding to the target fleet according to the management evaluation result of the target fleet.
For example, assuming that the management evaluation result of the target fleet is 70 points, the electronic device determines a target feature with a lower score according to the target feature corresponding to the target fleet, and then generates a management scheme corresponding to the target fleet according to the target feature with the lower score.
The motorcade management method provided by the embodiment of the invention obtains the corresponding target characteristics of a target motorcade in a target time period; the target characteristics include vehicle target characteristics, driver target characteristics, driving behavior target characteristics, and safety target characteristics in a target fleet. And each target feature is subjected to weighting processing to generate each weighting feature, so that the accuracy of the generated weighting features is ensured. Based on the weighted features, a first feature corresponding to the first level and a second feature corresponding to the second level are generated, so that the accuracy of the generated first feature corresponding to the first level and the generated second feature corresponding to the second level is guaranteed. Then, the management evaluation result of the target fleet is determined based on the relation between the target characteristic and the first characteristic and the second characteristic, and the accuracy of the determined management evaluation result is guaranteed. And then, a management scheme corresponding to the target fleet is generated based on the management evaluation result, so that the accuracy of the generated management scheme corresponding to the target fleet is ensured. The fleet management method takes the management of the vehicles and the drivers in the fleet as well as the management of the driving behaviors and the safety management into consideration, so that the finally generated management scheme corresponding to the target fleet can realize the management of the vehicles and the drivers in the target fleet and also realize the management of the driving behaviors and the safety management in the target fleet, thereby realizing the comprehensive and efficient management of the target fleet.
In an embodiment of the present application, as shown in fig. 2, a fleet management method is provided, which is described by taking an example of the method applied to an electronic device, and includes the following steps:
s21, acquiring the corresponding target characteristics of the target fleet in the target time period; the target characteristics include vehicle target characteristics, driver target characteristics, driving behavior target characteristics, and safety target characteristics in a target fleet.
Please refer to fig. 1 for description of S11 for this step, which is not described herein.
S22, a weighting process is performed on each target feature to generate each weighted feature.
Please refer to fig. 1 for a description of S12 for this step, which is not described herein.
S23, based on the weighted features, a first feature corresponding to the first rank and a second feature corresponding to the second rank are generated.
In an optional embodiment of the application, the step S23 "generating a first feature corresponding to the first level and a second feature corresponding to the second level based on the weighted features" may include the following steps:
s231, based on the value of each weighted feature corresponding to each time point, each weighted feature having the largest value is determined from the weighted features, and the first feature is generated.
Specifically, after calculating the weighted features corresponding to the target features, the electronic device determines, according to the values of the weighted features corresponding to the time points, each weighted feature having the largest value from among the weighted features, and generates the first feature.
For example, assume that one of the weighted features is whether the vehicle is maintaining on time, wherein a higher score for vehicle maintenance on time represents a better vehicle maintenance on time. And the electronic equipment determines whether the vehicle with the maximum value maintains the weighting characteristics on time according to the score of whether the vehicle maintains on time in the target time period. According to the method, each weighted feature having the largest value is determined in turn from the other weighted features, thereby generating a first feature.
S232, based on the value of each weighted feature corresponding to each time point, each weighted feature having the smallest value is determined from the weighted features, and the second feature is generated.
Specifically, after calculating the weighted features corresponding to the target features, the electronic device determines each weighted feature with the smallest value from the weighted features according to the value of each weighted feature corresponding to each time point, and generates the second feature.
For example, assume that one of the weighted characteristics is whether the vehicle is on time, wherein a lower score for on time maintenance of the vehicle represents a poorer on time maintenance of the vehicle. And the electronic equipment determines whether the vehicle with the minimum value maintains the weighted characteristic on time according to the score of whether the vehicle maintains on time in the target time period. According to the method, each weighted feature having the smallest value is determined in turn from the other weighted features, thereby generating the second feature.
And S24, determining a management evaluation result of the target fleet based on the relationship between the target characteristics and the first characteristics and the second characteristics, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
Please refer to fig. 1 for a description of S14 for this step, which is not described herein.
According to the fleet management method provided by the embodiment of the invention, each weighting characteristic with the maximum value is determined from each weighting characteristic based on the value of each weighting characteristic corresponding to each time point, so that the first characteristic is generated, and the accuracy of the generated first characteristic is ensured. Then, based on the values of the weighted features corresponding to the respective time points, each weighted feature having the smallest value is determined from the weighted features, and the second feature is generated. The accuracy of the generated second feature is guaranteed.
In an embodiment of the present application, as shown in fig. 3, a fleet management method is provided, which is described by taking an example of the method applied to an electronic device, and includes the following steps:
and S31, acquiring the target characteristics corresponding to the target fleet in the target time period.
The target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet.
For this step, please refer to fig. 2 for description of S21, which is not described herein.
S32, the target features are weighted to generate weighted features.
Please refer to fig. 2 for a description of S22 for this step, which is not repeated herein.
S33, based on the weighted features, a first feature corresponding to the first rank and a second feature corresponding to the second rank are generated.
For this step, please refer to fig. 2 for description of S23, which is not described herein.
And S34, determining a management evaluation result of the target fleet based on the relationship between the target characteristics and the first characteristics and the second characteristics, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
In an alternative embodiment of the present application, the step S34 "determining the management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic" may include the following steps:
and S341, calculating a first distance between the first feature and the target feature based on the first feature and the target feature.
Specifically, the electronic device may calculate a first distance between the first feature and the target feature based on the first feature and the target feature by using a preset algorithm.
Wherein the first distance may be a euclidean distance, a manhattan distance, a chebyshev distance, a mahalanobis distance, or the like. The first distance is not limited in the embodiments of the present application. The preset algorithm corresponds to the meaning of the first distance, and when the first distance is the Euclidean distance, the preset algorithm is an Euclidean distance calculation algorithm; when the first distance is a manhattan distance, the preset algorithm is a manhattan distance calculation algorithm, and the preset algorithm is not specifically limited in the embodiment of the application.
And S342, calculating a second distance between the second feature and the target feature based on the second feature and the target feature.
Specifically, the electronic device may calculate, using a preset algorithm, a second distance between the second feature and the target feature based on the second feature and the target feature.
Wherein the second distance may be a euclidean distance, a manhattan distance, a chebyshev distance, a mahalanobis distance, or the like. The second distance is not limited in the embodiments of the present application. Wherein, the first distance and the second distance can be the same or different. The preset algorithm corresponds to the meaning of the second distance, and when the second distance is the Euclidean distance, the preset algorithm is an Euclidean distance calculation algorithm; when the second distance is a manhattan distance, the preset algorithm is a manhattan distance calculation algorithm, and the preset algorithm is not specifically limited in the embodiment of the application.
For example, assuming that the first distance and the second distance are both euclidean distances, the electronic device may calculate the first distance and the second distance based on the following formulas:
wherein the content of the first and second substances,
the first characteristic is represented by the first characteristic,
a second characteristic is shown in the form of,
it is indicated that the first distance is,
denotes a second distance, YijFor each target feature, i represents a corresponding identifier of each target feature, and for example, assuming that the number of target features is 5, i represents 1, 2, 3, 4, 5. j identifies the time dimension corresponding to each target feature, where j may represent a monthFor example, month 1, month 2, month 3, j may also represent a date, such as month 1, month 2, month 3, etc.
And S343, determining a management evaluation result of the target fleet based on the first distance and the second distance.
In an optional embodiment of the present application, the step S343, "determining the management evaluation result of the target fleet based on the first distance and the second distance", may include the following steps:
and dividing the first distance by the sum of the first distance and the second distance to obtain a management evaluation result of the target fleet.
In an alternative embodiment of the present application, the electronic device may obtain the management evaluation result of the target fleet by dividing the first distance by the sum of the first distance and the second distance.
In another alternative embodiment of the present application, the electronic device may multiply the target value by the first distance and then divide the sum of the first distance and the second distance to obtain the management evaluation result of the target fleet. The target value may be 100, or may be other values, and the embodiment of the present application is not particularly limited.
For example, when the target value is 100, the electronic device may determine the management evaluation result of the target fleet based on the following formula:
wherein, CiAnd showing the management evaluation result of the target fleet.
Wherein, Ci∈(0，100)，CiThe larger the indication, the better the corresponding management level of the target fleet over the i-characterized time period, CiThe smaller the size, the worse the corresponding level of management of the target fleet over the i-characterized time period. The final score was classified as "to be improved" in the interval (0, 60); the final score was classified as "pass" in the interval [60, 74); the final score was classified as "good" in the interval [74, 84); the final score was classified as "excellent" in the interval [84, 100).
According to the fleet management method provided by the embodiment of the invention, the first distance between the first characteristic and the target characteristic is calculated based on the first characteristic and the target characteristic, so that the accuracy of the calculated first distance between the first characteristic and the target characteristic is ensured. And calculating a second distance between the second feature and the target feature based on the second feature and the target feature, so that the accuracy of the calculated second distance between the second feature and the target feature is ensured. And then, the first distance is divided by the sum of the first distance and the second distance to obtain a management evaluation result of the target motorcade, so that the accuracy of the calculated management evaluation result of the target motorcade is ensured.
In an embodiment of the present application, as shown in fig. 4, a fleet management method is provided, which is described by taking the method as an example for being applied to an electronic device, and includes the following steps:
and S41, acquiring the corresponding target characteristics of the target fleet in the target time period.
The target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet.
Please refer to fig. 3 for the description of S31 for this step, which is not repeated herein.
S42, a weighting process is performed on each target feature to generate each weighted feature.
In an optional embodiment of the present application, the step of performing weighting processing on each target feature to generate each weighted feature at S42 "may include the following steps:
and S421, calculating information entropy corresponding to each time point of each target feature in the target time period based on the relation between each target feature.
Specifically, the electronic device may identify each target feature in the target time period, then obtain a relationship between each target feature, and calculate an information entropy corresponding to each time point of each target feature in the target time period based on the relationship between each target feature.
In an alternative embodiment of the present application, the target feature is assumed to be YijIn whichI represents the corresponding identifier of each target feature, and for example, assuming that the number of target features is 5, i represents 1, 2, 3, 4, 5. j identifies the time dimension corresponding to each target feature, where j may represent a month, such as 1 month, 2 months, 3 months, and j may also represent a date, such as a month number 1, 2, 3, etc.
The electronic device may calculate the information entropy of each target feature corresponding to each time point in the target time period by using the following formula:
wherein, EjAnd representing the information entropy of each target feature corresponding to each time point in the target time period. Illustratively, assuming j represents a month, e.g., 1 month, 2 months, 3 months, then E1The information entropy corresponding to each target feature in 1 month is represented.
S422, calculating the weight information of each target feature corresponding to each time point based on the relation between each information entropy and each target feature.
In an optional embodiment of the present application, the electronic device may multiply each target feature by the information entropy of each target feature at each time point, so as to calculate the weight information of each target feature at each time point.
For example, the electronic device may multiply each target feature corresponding to month 1 by the information entropy corresponding to month 1, so as to calculate the weight information corresponding to each target feature at each time point.
In another optional embodiment of the present application, the step S232 "of calculating the weight information corresponding to each target feature at each time point based on the relationship between each information entropy and each target feature" may include the following steps:
(1) and subtracting the information entropy corresponding to each time point by using the target parameter to obtain the target information entropy corresponding to each time point.
Specifically, the electronic device may obtain a target parameter, where the target parameter may be set according to an actual situation. The target parameter may be 1 or 2, and the target parameter is not specifically limited in this embodiment of the present application.
(2) And dividing the target information entropy by the sum of the target information entropies to obtain the weight information of each target feature corresponding to each time point.
Specifically, after calculating each target information entropy, the electronic device divides each target information entropy by the sum of each target information entropy to obtain the weight information of each target feature corresponding to each time point based on each target information entropy.
For example, the electronic device may subtract the information entropy corresponding to each time point by 1 to obtain a target information entropy corresponding to each time point, and then divide the sum of the target information entropies by the target information entropy to obtain the weight information of each target feature corresponding to each time point, where a specific formula may be as follows:
wherein, WjAnd weight information representing the corresponding weight of each target feature at each time point. Illustratively, assuming j represents a month, e.g., 1 month, 2 months, 3 months, then W1The weight information corresponding to each target feature in 1 month is represented.
S423, each target feature is multiplied by the weight information to generate each weighted feature.
Specifically, after calculating the weight information corresponding to each target feature at each time point, the electronic device may multiply each target feature by the weight information according to the time point corresponding to each target feature to generate each weighted feature.
Illustratively, the following equation is shown:
wherein r isijFor each weighted feature, R ═ Rij)n×mFor each weightA weighted feature matrix of feature generation.
S43, based on the weighted features, a first feature corresponding to the first rank and a second feature corresponding to the second rank are generated.
Please refer to fig. 3 for the description of S33 for this step, which is not repeated herein.
And S44, determining a management evaluation result of the target fleet based on the relationship between the target characteristics and the first characteristics and the second characteristics, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
Please refer to fig. 3 for a description of S34 for this step, which is not described herein.
According to the fleet management method provided by the embodiment of the invention, the information entropy corresponding to each time point of each target feature in the target time period is calculated based on the relationship among the target features, so that the accuracy of the calculated information entropy corresponding to each time point of each target feature is ensured. And then, subtracting the information entropy corresponding to each time point by using the target parameter to obtain the target information entropy corresponding to each time point, thereby ensuring the accuracy of the target information entropy corresponding to each time point obtained by calculation. Then, based on each target information entropy, dividing the sum of each target information entropy to obtain the weight information of each target characteristic corresponding to each time point, and ensuring the accuracy of the obtained weight information of each target characteristic corresponding to each time point. And finally, multiplying each target feature by the weight information to generate each weighted feature, thereby ensuring the accuracy of each generated weighted feature.
In an embodiment of the present application, as shown in fig. 5, a fleet management method is provided, which is described by taking the method as an example for being applied to an electronic device, and includes the following steps:
and S51, acquiring the corresponding target characteristics of the target fleet in the target time period.
The target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet.
In an optional embodiment of the present application, the step of obtaining the target characteristic corresponding to the target fleet in the target time period in S51 may include the following steps:
and S511, acquiring corresponding basic characteristics of the target fleet in the target time period.
The basic characteristics comprise basic characteristics of vehicles in a target fleet, basic characteristics of drivers, basic characteristics of driving behaviors and basic characteristics of safety.
The basic characteristics of the driver can include age information of the driver, the working years, working driving records (such as violation records, accident records and the like), physical health conditions and the like, and the overall level of the driver can be measured through the basic characteristics of the driver. The basic characteristics of the vehicle can comprise whether the vehicle is maintained on time, whether the vehicle sanitation is cleaned according to the regulation, whether vehicle-mounted tools are complete, whether vehicle files are maintained normally, whether a worker works on time on a working day and the like. The basic driving behavior characteristics may include whether the driver is tired driving, the number of rapid accelerations, the number of rapid decelerations, the number of rapid turns, the fuel consumption of hundreds of kilometers, and the like. The safety basic characteristics can comprise red light running times of a driver, electronic fence exit times, abnormal oil consumption times, oil stealing and material stealing times, abnormal discharging times and the like, and whether the safety consciousness of the driver is weak or not can be measured through the safety basic characteristics.
Specifically, the electronic device may receive a basic feature corresponding to a target fleet within a target time period, which is input by a user; the electronic device can also receive basic characteristics, which are sent by other devices, of the target fleet in the target time period. The implementation of the application is not particularly limited to the way in which the electronic device acquires the corresponding basic features of the target fleet in the target time period.
And S512, normalizing each basic feature to generate each target feature corresponding to each basic feature.
In an optional embodiment of the application, after acquiring the corresponding basic features of the target fleet in the target time period, the electronic device may clean the acquired basic feature data, delete some disordered data, perform median substitution processing on some abnormal data, and perform ordered coding or One-hot coding processing on category data.
In one embodiment of the present application, the electronic device may further construct some other useful basic features according to the currently acquired basic features. For example, the number of rapid accelerations, rapid decelerations, rapid turns, etc. of the driver.
The electronic device can then analyze each of the base features to determine whether each of the base features is a earned feature or a negative feature. The positive characteristics indicate that the numerical value corresponding to the basic characteristics is larger, the better, for example, the vehicle operation rate, and the larger the value, the better the fleet management is; the negative characteristics indicate that the smaller the numerical value corresponding to the basic characteristics, the better, for example, the fuel consumption per hundred kilometers, and the larger the numerical value, the larger the space for fleet management promotion.
And the electronic equipment normalizes each basic feature according to the characteristic of each basic feature to generate each target feature corresponding to each basic feature, so that the influence of dimensional quantity on a calculation result is eliminated.
For example, for a basic feature of a positive feature, the electronic device may normalize the basic feature using the following formula:
for the basic features of the negative features, the electronic device may normalize the basic features using the following formula:
wherein XijAnd basic features are expressed, wherein i represents the corresponding identification of each target feature, and for example, assuming that the number of the target features is 5, i represents 1, 2, 3, 4 and 5. j identifies the time dimension corresponding to each target feature, where j may represent a month, such as 1 month, 2 months, 3 months, and j may also represent a date, such as a month number 1, 2, 3, etc.
S52, a weighting process is performed on each target feature to generate each weighted feature.
Please refer to fig. 4 for a description of S42 for this step, which is not described herein.
S53, based on the weighted features, a first feature corresponding to the first rank and a second feature corresponding to the second rank are generated.
Please refer to fig. 4 for a description of S43 for this step, which is not repeated herein.
And S54, determining a management evaluation result of the target fleet based on the relationship between the target characteristics and the first characteristics and the second characteristics, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
Please refer to fig. 4 for a description of S44 for this step, which is not described herein.
According to the fleet management method provided by the embodiment of the invention, the corresponding basic characteristics of the target fleet in the target time period are obtained, then the normalization processing is carried out on the basic characteristics, and the target characteristics corresponding to the basic characteristics are generated, so that the accuracy of the target characteristics corresponding to the generated basic characteristics is ensured.
It should be understood that although the various steps in the flow diagrams of fig. 1-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
As shown in fig. 6, the present embodiment provides a fleet management device, including:
the acquisition module 61 is used for acquiring target characteristics corresponding to a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in a target fleet;
a first generating module 62, configured to perform weighting processing on each target feature to generate each weighted feature;
a second generating module 63, configured to generate, based on each weighted feature, a first feature corresponding to the first level and a second feature corresponding to the second level;
and the determining module 64 is used for determining a management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
In an embodiment of the present application, the second generating module 63 is specifically configured to determine, based on values of the weighted features corresponding to the time points, each weighted feature with a maximum value from the weighted features, and generate the first feature; based on the values of the weighted features corresponding to the time points, the weighted features with the smallest values are determined from the weighted features, and the second feature is generated.
In an embodiment of the present application, the determining module 64 is specifically configured to calculate a first distance between the first feature and the target feature based on the first feature and the target feature; calculating a second distance between the second feature and the target feature based on the second feature and the target feature; and determining a management evaluation result of the target fleet based on the first distance and the second distance.
In an embodiment of the present application, the determining module 64 is specifically configured to obtain the management evaluation result of the target fleet by dividing the first distance by the sum of the first distance and the second distance.
In an embodiment of the present application, the first generating module 62 is specifically configured to calculate an information entropy corresponding to each time point of each target feature in the target time period based on a relationship between the target features; calculating the weight information of each target feature corresponding to each time point based on the relation between each information entropy and each target feature; the weighted features are generated by multiplying the weighted information by the target features.
In an embodiment of the present application, the first generating module 62 is specifically configured to subtract the information entropy corresponding to each time point from the target parameter to obtain a target information entropy corresponding to each time point; and dividing the target information entropy by the sum of the target information entropies to obtain the weight information of each target feature corresponding to each time point.
In an embodiment of the present application, the obtaining module 61 is specifically configured to obtain corresponding basic features of a target fleet in a target time period; and carrying out normalization processing on each basic feature to generate each target feature corresponding to each basic feature.
For specific limitations and advantages of the fleet management device, reference may be made to the above limitations of the fleet management method, which are not described herein again. The modules in the fleet management device can be implemented in whole or in part by software, hardware, and combinations thereof. The modules may be embedded in a hardware form or may be independent of a processor in the electronic device, or may be stored in a memory in the electronic device in a software form, so that the processor calls and executes operations corresponding to the modules.
An embodiment of the present invention further provides an electronic device, which has the fleet management apparatus shown in fig. 6.
As shown in fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present invention, and as shown in fig. 7, the electronic device may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, memory 74, at least one communication bus 72. Wherein a communication bus 72 is used to enable the connection communication between these components. The communication interface 73 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 73 may also include a standard wired interface and a standard wireless interface. The Memory 74 may be a high-speed RAM Memory (volatile Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 74 may alternatively be at least one memory device located remotely from the processor 71. Wherein the processor 71 may be in connection with the apparatus described in fig. 6, an application program is stored in the memory 74, and the processor 71 calls the program code stored in the memory 74 for performing any of the above-mentioned method steps.
The communication bus 72 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 72 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
The memory 74 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 74 may also comprise a combination of the above-mentioned kinds of memories.
The processor 71 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 71 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), General Array Logic (GAL), or any combination thereof.
Optionally, the memory 74 is also used for storing program instructions. The processor 71 may invoke program instructions to implement a fleet management method as shown in the embodiments of fig. 1-5 of the present application.
The embodiment of the invention also provides a transport vehicle, which comprises a vehicle body and the electronic equipment shown in fig. 7, wherein the electronic equipment can execute the fleet management method shown in the embodiments of fig. 1 to 5 in the application. The transport vehicle may be a lead vehicle for travel by a target fleet.
The embodiment of the invention also provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the fleet management method in any method embodiment. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A fleet management method, comprising:
acquiring target characteristics corresponding to a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in the target fleet;
performing weighting processing on each target feature to generate each weighted feature;
generating a first feature corresponding to a first level and a second feature corresponding to a second level based on each of the weighted features;
and determining a management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
2. The method of claim 1, wherein generating a first feature corresponding to a first level and a second feature corresponding to a second level based on each of the weighted features comprises:
determining each weighted feature with the maximum value from each weighted feature based on the value of each weighted feature corresponding to each time point, and generating the first feature;
and determining each weighted feature with the smallest value from the weighted features based on the value of each weighted feature corresponding to each time point, and generating the second feature.
3. The method of claim 1, wherein determining a management evaluation result for the target fleet of vehicles based on the relationship of the target characteristic to the first characteristic and the second characteristic comprises:
calculating a first distance between the first feature and the target feature based on the first feature and the target feature;
calculating a second distance between the second feature and the target feature based on the second feature and the target feature;
and determining a management evaluation result of the target fleet based on the first distance and the second distance.
4. The method of claim 3, wherein determining the management rating of the target fleet based on the first distance and the second distance comprises
And dividing the first distance by the sum of the first distance and the second distance to obtain a management evaluation result of the target fleet.
5. The method of claim 1, wherein the performing a weighting process on each of the target features to generate each weighted feature comprises:
calculating information entropy corresponding to each time point of each target feature in the target time period based on the relationship between each target feature;
calculating weight information corresponding to each target feature at each time point based on the relation between each information entropy and each target feature;
multiplying each of the target features by the weight information to generate each of the weighted features.
6. The method according to claim 2, wherein the calculating of the weight information of each target feature corresponding to each time point based on the relationship between each information entropy and each target feature comprises:
subtracting the information entropy corresponding to each time point by using a target parameter to obtain a target information entropy corresponding to each time point;
and dividing the sum of each target information entropy by each target information entropy based on each target information entropy to obtain the weight information of each target feature corresponding to each time point.
7. The method of claim 1, wherein the obtaining of the corresponding target characteristics of the target fleet over the target time period comprises:
acquiring corresponding basic features of the target fleet in the target time period;
and carrying out normalization processing on each basic feature to generate each target feature corresponding to each basic feature.
8. A fleet management device, comprising:
the acquisition module is used for acquiring corresponding target characteristics of a target fleet in a target time period; the target characteristics comprise vehicle target characteristics, driver target characteristics, driving behavior target characteristics and safety target characteristics in the target fleet;
the first generation module is used for performing weighting processing on each target feature to generate each weighted feature;
a second generating module, configured to generate, based on each of the weighted features, a first feature corresponding to the first level and a second feature corresponding to the second level;
and the determining module is used for determining a management evaluation result of the target fleet based on the relationship between the target characteristic and the first characteristic and the second characteristic, and generating a management scheme corresponding to the target fleet based on the management evaluation result.
9. An electronic device, wherein the electronic device is a cloud server or/and a vehicle controller, and the electronic device comprises a memory and a processor, wherein the memory stores computer instructions, and the processor executes the computer instructions to perform the fleet management method according to any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the fleet management method of any of claims 1-7.
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