CN111079008A - Method and system for recommending scheme of taxi driver staying in storage pool - Google Patents
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Abstract
The invention provides a method and a system for recommending a scheme for a taxi driver to stay in a storage pool. The method for recommending the leaving scheme of the taxi driver in the storage pool comprises the steps of constructing a decision evaluation matrix according to a leaving decision scheme faced by the taxi driver and an evaluation index in each leaving decision scheme, and carrying out standardized treatment on the decision evaluation matrix; screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix; calculating the boundary distance between the evaluation index and the optimal decision-making scheme and the worst decision-making scheme respectively
And
calculating the close value c of the evaluation index and each leaving decision schemeiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers; for the closeness value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
Description
Technical Field
The invention belongs to the field of recommendation of leaving schemes in a storage pool, and particularly relates to a method and a system for recommending leaving schemes of taxi drivers in the storage pool.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Because an airport or a high-speed rail station is generally far away from a downtown, particularly public transportation modes such as the airport, a bus, a subway and the like are inconvenient, and besides, passengers carry more luggage, and the public transportation stops at night, the taxi at the airport is a main transportation mode after the passengers arrive at a ground junction. The airport separates the passage of the passenger (departure) and the passage of the passenger (arrival), and the taxi driver of the passenger faces the problem of leaving the storage pool, namely the taxi driver has to select between waiting for the passenger in the storage pool and returning the empty taxi to the urban area for the passenger.
The inventor finds that at present, for the judgment of the 'storage pool' leaving, a driver usually judges which choice brings greater benefit to the driver by past experience, but subjective decision-making usually has greater uncertainty, so that greater imbalance is brought to the benefit of the driver, and the design and distribution of lanes and parking points of the 'storage pool' of the airport are also influenced.
Disclosure of Invention
In order to solve the above problems, a first aspect of the present invention provides a method for recommending a taxi driver leaving scheme in a storage pool, which considers the affinity values between the evaluation indexes in the leaving decision schemes and the leaving decision schemes, and screens out an optimal leaving decision scheme and recommends a taxi driver according to the fact that the smaller the affinity value is, the better the evaluation result of the corresponding leaving decision scheme is, thereby assisting the taxi driver in making a decision for increasing the benefit of the taxi driver and automatically recommending the leaving decision scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a scheme recommendation method for a taxi driver to stay in a storage pool comprises the following steps:
constructing a decision evaluation matrix according to a leaving decision scheme faced by a taxi driver and an evaluation index in each leaving decision scheme, and carrying out standardized processing on the decision evaluation matrix;
screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
calculating the boundary distance d between the evaluation index and the optimal decision-making scheme and the worst decision-making scheme respectivelyi + and di -；
Calculating the close value c of the evaluation index and each leaving decision schemeiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
for the closeness value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
In order to solve the above problems, a second aspect of the present invention provides a scheme recommendation system for taxi drivers to stay in a storage pool, which considers the close values between the evaluation indexes in the stay-away decision schemes and the stay-away decision schemes, and screens out the optimal stay-away decision scheme and recommends the taxi drivers according to the better evaluation result of the stay-away decision scheme when the close value is smaller, thereby assisting the taxi drivers to make decisions on greater benefits and automatically recommend the stay-away decision scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a scheme recommendation system for taxi drivers to stay in a storage pool comprises:
the decision evaluation matrix construction and normalization module is used for constructing a decision evaluation matrix according to the leaving decision schemes faced by taxi drivers and the evaluation indexes in each leaving decision scheme, and carrying out normalized processing on the decision evaluation matrix;
the decision scheme optimal and worst screening module is used for screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
a boundary distance calculation module for calculating the boundary distance between the evaluation index and the optimal decision scheme and the worst decision scheme respectively
And
；
an affinity calculation module for calculating affinities c of the evaluation index and the respective stay away decision schemesiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
a stay away decision scheme recommendation module for recommending the affinity value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
In order to solve the above problems, a third aspect of the present invention provides a computer-readable storage medium, which considers the closeness values between the evaluation indexes in the leaving decision schemes and the respective leaving decision schemes, and screens out the optimal leaving decision scheme and recommends the optimal leaving decision scheme to the taxi driver according to the fact that the smaller the closeness value is, the better the evaluation result of the corresponding leaving decision scheme is, thereby enabling the taxi driver to make a decision for increasing the benefit of the taxi driver and automatically recommend the leaving decision scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for recommending a taxi driver's scenario for staying in a pool as described above.
In order to solve the above problems, a fourth aspect of the present invention provides a computer device, which considers the closeness values between the evaluation indexes in the leaving decision schemes and the leaving decision schemes, and selects the best leaving decision scheme and recommends to a taxi driver according to that the closer the closeness value is, the better the corresponding leaving decision scheme evaluation result is, thereby implementing a decision assisting the taxi driver to make a greater benefit to the taxi driver and an automatic recommendation of the leaving decision scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for recommending a taxi driver's plan to stay in a pool as described above when executing the program.
The invention has the beneficial effects that:
according to the leaving decision schemes faced by taxi drivers and the evaluation indexes in each leaving decision scheme, a decision evaluation matrix is constructed, and the decision evaluation matrix is subjected to standardized processing; screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix; calculating the evaluation indexes respectively and optimallyBoundary distance d between decision scheme and worst decision schemei -Calculating the closeness values of the evaluation indexes and each stay-away decision scheme, and sorting according to the closeness values, wherein the smaller the closeness value is, the better the evaluation result of the corresponding stay-away decision scheme is; and recommending the leaving decision scheme with the minimum closeness value to the taxi driver, so that the taxi driver is assisted to make a decision for increasing the benefit of the taxi driver and the automatic recommendation of the leaving decision scheme are realized.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for recommending a taxi driver stay in a storage pool according to an embodiment of the invention;
FIG. 2 is a graph of factor indicators affecting driver decision scheme selection according to an embodiment of the present invention;
FIG. 3 is a weather indicator for an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example 1
Fig. 1 shows a flowchart of a method for recommending a taxi driver to stay in a storage pool according to the embodiment.
The following describes in detail a specific implementation process of the scheme recommendation method for a taxi driver to stay in a storage pool in this embodiment with reference to fig. 1:
as shown in fig. 1, the method for recommending a taxi driver to stay in a storage pool in this embodiment includes:
step S101: constructing a decision evaluation matrix according to a leaving decision scheme faced by a taxi driver and an evaluation index in each leaving decision scheme, and carrying out standardized processing on the decision evaluation matrix;
in the specific implementation, two core factors of driver income and passenger number are considered, wherein the driver income comprises a cost index, a benefit index and a fixity index; the number of passengers includes the number of flight arrivals per unit time and the seating rate. The cost indexes comprise oil consumption and personal income tax, the benefit indexes comprise passenger carrying mileage and passenger carrying times, and the fixity indexes comprise contract expenses and parking fees of the storage pool. The number of arriving flights per unit time is influenced by weather conditions, seasons, day and night, and the like, and the passenger seat rate includes high peaks and low peaks.
The taxi driver income is influenced by necessary expenses such as passenger number, waiting time, passenger driving mileage, oil consumption, total amount of taxi fee, personal income tax, taxi contract fee and the like, and waiting fee in the storage battery, wherein the passenger driving mileage and the passenger number are in positive correlation with the taxi income, and the waiting time and the oil consumption are in negative correlation with the driver income. Generally, the total amount of the fare increases, and the income of the driver increases; the more cost it takes to enter the storage pool, the lower the profit. But comparatively speaking, the cost of entering the storage pool has less negative correlation influence on the income of the driver; the taxi contract cost is necessary expenditure, and objectivity is achieved; oil consumption and personal income tax are necessary expenses, and the expenses are positively correlated with the driving mileage and further positively correlated with the income of a driver, but the positive correlation degree is lower relative to the driving mileage.
After looking up the taxi charging standard, the taxi charging is divided into two charging standards of daytime and nighttime, and the charging standard is obtained from related data, the taxi taking cost of passengers is distributed in a piecewise function according to different mileage, and the following two piecewise functions can be obtained through calculation and are shown in formulas (1) and (2):
wherein x represents mileage; f. of1Indicating a daytime charge; f. of2Indicating a night charge.
The total cost W is defined as shown in equation (3):
W＝c+d+m+T (3)
(3) in the formula: c is fuel consumption; d is the cost required for entering the storage pool; m is the taxi contract fee; t is the time cost.
The driver net income P is defined as shown in equation (4):
P＝f-W (4)
(4) in the formula: f is the total revenue for the driver.
The system can calculate taxi taking cost and consumed time cost of a driver according to taxi driving mileage and waiting time, wherein the longer the waiting time is, the larger the time cost is, the net benefit of the driver can be calculated by the formula (4) by combining taxi contract fee to be paid of the taxi driver, the cost of entering a storage pool and the like, if the net benefit is higher than a certain fixed value, the driver selects a certain scheme (namely entering the storage pool to wait for passengers), and if the net benefit is lower than the certain fixed value, the driver selects another scheme (namely directly returning empty cars to the urban area to carry passengers).
If a driver is faced with m leave decision schemes, each scheme has n evaluation indexes, the decision evaluation matrix R is:
in the process of the standardization processing of the decision evaluation matrix:
for the benefit type index: a isij＝xij/maxxij；(5)
For cost-type indicators: a isij＝minxij/xij；(6)
wherein ,xijFor deciding elements in the evaluation matrix, aijAnd evaluating elements in the matrix for the normalized strategy.
The waiting time and the oil consumption are cost indexes, the driving mileage of the passenger is benefit indexes, and the contract expense of the taxi is fixed indexes. The net revenue for the taxi driver is equal to the passenger's taxi fee minus the total cost (time cost + taxi contract cost and personal income tax + waiting cost in the pool + fuel consumption).
And further calculating a decision matrix after the normalization processing:
step S102: screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
the optimal decision scheme at this time is:
the worst decision scheme is: a. the-＝(y1 -,y2 -,...,ym -) (10)
The number of elements of the optimal decision scheme and the worst decision scheme is m, and is equal to the total number of leave decision schemes faced by a driver; the elements in the optimal decision scheme are maximum values in each row of elements in the decision evaluation matrix after normalization processing; the element in the worst decision scheme is the minimum value in each row of elements in the decision evaluation matrix after the normalization processing.
Step S103: calculating the boundary distance between the evaluation index and the optimal decision-making scheme and the worst decision-making scheme respectively
And
in specific implementation, the boundary distances between the evaluation indexes and the optimal decision-making scheme and the worst decision-making scheme respectively are calculated
And
the formula of (1) is:
wherein ,
elements in the optimal decision scheme set;
elements in the worst decision scheme set; a isijRepresenting the jth normalized evaluation index corresponding to the ith leaving decision scheme faced by the taxi driver as an element in the normalized decision evaluation matrix; n is the number of evaluation indexes in each stay away decision scheme.
Step S104: calculating the close value c of the evaluation index and each leaving decision schemeiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
step S105: for the closeness value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
According to the closeness value ciSize ordering of ciThe smaller the number of the arriving flights in the time period, the better the evaluation result of the scheme is, the more the number of the queuing vehicles ahead and other influence factors are basically in the acceptable range, so that the probability that a taxi driver selects to enter an airport for queuing to carry passengers is higher; otherwise, ciThe smaller the evaluation result is, the worse the evaluation result is, and the more probable the driver returns the empty car to the urban area for carrying passengers.
The comparison of the schemes of the factors of the data decision matrix adopted in the present example, as shown in fig. 2, affects the factor indexes selected by the driver decision scheme. Wherein, when a driver faces a long passenger-carrying journey (30/km), a long waiting time (0.6h), a peak-off period of the number of passengers, the weather is in a low cloud state, and the passenger seat rate exceeds a first preset threshold value, when the passenger carrying journey is short (10/km), the waiting time is short (0.25h), the number of passengers is in a peak period, the weather is in a low cloud state, and the passenger seat rate exceeds a first preset threshold value, when the passenger carrying journey is very short (8/km), the waiting time is very short (0.2h), the peak period of the number of passengers is long, the weather is good, the passenger seat rate is high, the possibility that passengers are waiting to return to the urban area in a queue for going to the arrival area under the conditions of long passenger carrying journey (25/km), long waiting time (1h), low passenger number peak, low weather cloud state, and general passenger seat rate (exceeding a second preset threshold value and being smaller than a first preset threshold value, wherein the second preset threshold value is smaller than the first preset threshold value). Fig. 3 shows the weather conditions in the evaluation index.
The ideal situation that the driver goes to the arrival area and waits for the passengers to return to the urban area is that the degree of closeness with the ideal situation is respectively the four given classical situations, so the driver has four choices in the face of the four situations:
TABLE 1 decision scheme index
Obviously, the method is a multi-target decision problem with multi-factor indexes. The decision evaluation matrix is as follows:
the waiting time and the oil consumption are cost indexes, the driving mileage of the passenger is benefit indexes, and the contract cost of the taxi is fixed indexes. The net revenue for the taxi driver is equal to the passenger's taxi fee minus the total cost (time cost + taxi contract cost and personal income tax + waiting cost in the pool + fuel consumption).
The normalized matrix can be calculated by the above formula as:
the optimal decision scheme at this time is: a. the+＝(1,1,1,1,1)；
The worst decision scheme is: a. the-＝(1/5,4/15,4/7,5/8,5/8)；
Further, the following can be calculated:
calculating the osculation value:
the smaller the closeness value, the better the evaluation result, and the final evaluation result is: c > B > A > D.
In other embodiments, in order to verify the stability of the system and the effectiveness of the decision model, an affinity method can be used to summarize the situation that a taxi driver waits for passengers in an airport, and A, B, C, D, E, F, G, H, I schemes including longer waiting time, longer journey, peak in flight, better weather, better passenger seat rate, shorter waiting time of the driver, shorter journey, peak in flight, better weather, and higher passenger seat rate are obtained. The specific situation after each index of the scheme is quantized is as follows:
TABLE 2 decision matrix factors
By using the scheme recommendation method flow shown in fig. 1 for a taxi driver to stay in a storage pool, the obtained close values of the nine schemes are respectively as follows: 0.85409,0.28813,0.27516,1.0374,1.1051,1.5674,0,1.3297,0.55395. The close value of the G scheme is 0, so that the income of a driver of the G scheme is the highest, the situation that waiting time, driving mileage, weather, the number of flights and the passenger seat rate are good or excellent is known in the G scheme through image reading, according to actual experience, the situation is the optimal situation met by a taxi driver, and in the situation, the driver needs to select to wait. The situation of reading the graph to know the next closest value is the C, B, I scheme, and the decision scheme given by the comprehensive C, B, I scheme is as follows: under the conditions of good weather conditions and more flights, the driver can obtain good income per unit time no matter how far the passenger takes the bus.
The scheme with a large close value can be obtained by comprehensive analysis in the embodiment, and under the conditions of bad weather state, less number of flights and low passenger seat rate, the driver has less income, but the condition that the passengers with longer passenger journey can be received and better income is obtained is not excluded. Under the condition, the driver can comprehensively consider the number of taxis waiting in the storage pool to make a decision, if the number of the taxis in the storage pool is large, the driver can choose to return to the urban area, and if the number of the taxis in the storage pool is small, the driver can choose to continue waiting.
Example 2
The embodiment provides a scheme recommendation system that taxi driver stays in storage battery, and it includes:
(1) the decision evaluation matrix construction and normalization module is used for constructing a decision evaluation matrix according to the leaving decision schemes faced by taxi drivers and the evaluation indexes in each leaving decision scheme, and carrying out normalized processing on the decision evaluation matrix;
(2) the decision scheme optimal and worst screening module is used for screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
(3) a boundary distance calculation module for calculating the boundary distance between the evaluation index and the optimal decision scheme and the worst decision scheme respectively
And
specifically, in the boundary distance calculation module, the boundary distances between the evaluation indexes and the optimal decision-making scheme and the worst decision-making scheme are calculated respectively
And
the formula of (1) is:
wherein ,
elements in the optimal decision scheme set;
elements in the worst decision scheme set; a isijRepresenting the jth normalized evaluation index corresponding to the ith leaving decision scheme faced by the taxi driver as an element in the normalized decision evaluation matrix; n is the number of evaluation indexes in each stay away decision scheme.
(4) An affinity calculation module for calculating affinities c of the evaluation index and the respective stay away decision schemesiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
(5) a stay away decision scheme recommendation module for recommending the affinity value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
In the decision evaluation matrix construction and normalization module, evaluation indexes in a decision scheme are left to include cost indexes, benefit indexes and fixed indexes.
In the decision evaluation matrix construction and normalization module, in the process of normalizing the decision evaluation matrix:
for the benefit type index: a isij＝xij/maxxij；
For cost-type indicators: a isij＝minxij/xij；
wherein ,xijFor deciding elements in the evaluation matrix, aijTo a normalized placeThe elements in the matrix are evaluated by the processed strategy.
Example 3
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the scenario recommendation method for a taxi driver to stay in a pool as shown in fig. 1.
Example 4
The present embodiment provides a computer apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the steps of the method for recommending a taxi driver to reserve a pool of taxis as shown in fig. 1, as will be appreciated by those skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A scheme recommendation method for a taxi driver to stay in a storage pool is characterized by comprising the following steps:
constructing a decision evaluation matrix according to a leaving decision scheme faced by a taxi driver and an evaluation index in each leaving decision scheme, and carrying out standardized processing on the decision evaluation matrix;
screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
calculating evaluation indexes respectively and optimallyBoundary distance between policy scheme and worst-case policy scheme
And
calculating the close value c of the evaluation index and each leaving decision schemeiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
for the closeness value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
2. The method of claim 1, wherein the distance between the evaluation index and the boundary between the optimal decision-making scheme and the worst decision-making scheme is calculated
And
the formula of (1) is:
wherein ,
elements in the optimal decision scheme set;
elements in the worst decision scheme set; a isijThe elements in the standardized decision evaluation matrix represent jth standardized evaluation indexes corresponding to the ith leaving decision scheme faced by the taxi driver, and the total number of the evaluation indexes in the decision evaluation matrix is m; n is the number of evaluation indexes in each stay away decision scheme.
3. The method as claimed in claim 1, wherein the evaluation index of the decision scheme for leaving includes cost index, benefit index and fixed index.
4. The method for recommending a taxi driver's stay in a pool according to claim 3, wherein in the process of normalizing the decision evaluation matrix:
for the benefit type index: a isij＝xij/max xij；
For cost-type indicators: a isij＝min xij/xij；
wherein ,xijFor deciding elements in the evaluation matrix, aijElements in the strategy evaluation matrix after the normalization processing; the total number of the evaluation indexes in the decision evaluation matrix is m; the total number of stay-behind decision schemes in the decision evaluation matrix is n.
5. A scheme recommendation system for a taxi driver to stay in a storage pool is characterized by comprising:
the decision evaluation matrix construction and normalization module is used for constructing a decision evaluation matrix according to the leaving decision schemes faced by taxi drivers and the evaluation indexes in each leaving decision scheme, and carrying out normalized processing on the decision evaluation matrix;
the decision scheme optimal and worst screening module is used for screening out an optimal decision scheme and a worst decision scheme in the normalized decision evaluation matrix;
a boundary distance calculation module for calculating the boundary distance between the evaluation index and the optimal decision scheme and the worst decision scheme respectively
And
an affinity calculation module for calculating affinities c of the evaluation index and the respective stay away decision schemesiThe expression is as follows:
wherein i is more than or equal to 1 and less than or equal to m, and m is the number of leaving decision schemes faced by taxi drivers;
a stay away decision scheme recommendation module for recommending the affinity value ciSorting is performed, wherein the closeness value ciThe smaller the size, the better the corresponding decision-making scheme evaluation result; will closely value ciAnd recommending the leaving decision scheme corresponding to the minimum value to the car rental driver.
6. The system of claim 5, wherein the boundary distance calculation module calculates boundary distances between the evaluation index and the optimal decision-making scheme and the worst decision-making scheme respectively
And
the formula of (1) is:
wherein ,
elements in the optimal decision scheme set;
elements in the worst decision scheme set; a isijThe elements in the standardized decision evaluation matrix represent jth standardized evaluation indexes corresponding to the ith leaving decision scheme faced by the taxi driver, and the total number of the evaluation indexes in the decision evaluation matrix is m; n is the number of evaluation indexes in each stay away decision scheme.
7. The system for recommending a taxi driver stay in a storage pool according to claim 5, wherein in said decision evaluation matrix construction and normalization module, evaluation indexes in the stay decision scheme include cost index, benefit index and fixed index.
8. The system of claim 7, wherein the decision evaluation matrix building and normalizing module, during the process of normalizing the decision evaluation matrix, is configured to:
for the benefit type index: a isij＝xij/max xij；
For cost-type indicators: a isij＝min xij/xij；
wherein ,xijFor deciding elements in the evaluation matrix, aijElements in the strategy evaluation matrix after the normalization processing; the total number of the evaluation indexes in the decision evaluation matrix is m; leave decision scheme aggregation in decision evaluation matrixThe number is n.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for recommending a scenario for a taxi driver to leave a pool according to any one of claims 1 to 4.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for recommending a taxi driver to leave a pool.
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