CN109359966A - A kind of method and apparatus of detection logistics package charging exception - Google Patents
A kind of method and apparatus of detection logistics package charging exception Download PDFInfo
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- CN109359966A CN109359966A CN201811038911.8A CN201811038911A CN109359966A CN 109359966 A CN109359966 A CN 109359966A CN 201811038911 A CN201811038911 A CN 201811038911A CN 109359966 A CN109359966 A CN 109359966A
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Abstract
The present invention is suitable for logistics management technical field, provides a kind of method of detection logistics package charging exception, the described method comprises the following steps: according to the correlated characteristic data selection criteria feature of fee properties and the mapping table of correlated characteristic；According to the standard feature and to the data in the mapping table of fee properties and correlated characteristic, the characteristic statistics model of history logistics data is established, the characteristic statistics model is for indicating that the three of standard feature, fee properties classification threshold value corresponding with each fee properties classification is associated with；The standard feature and fee properties of current package are obtained, and according to the standard feature of current package and the characteristic statistics model, obtains the corresponding threshold value of the affiliated fee properties classification of current package；According to the fee properties of current package and the corresponding threshold value of the affiliated fee properties classification of current package, determine the fee properties of current package with the presence or absence of abnormal.
Description
Technical field
The method of charging exception is wrapped up the invention belongs to logistics management technical field more particularly to a kind of detection logistics and is set
It is standby.
Background technique
In existing logistics management equipment, the logistics cost of each package is according to departure place, destination, and package is big
Small, package weight, logistics mode, the data informations such as duration are calculated, but existing logistics management equipment can not detect
Whether the logistics cost being calculated is rationally and accurate, and it is abnormal whether logistics cost can exist.
Summary of the invention
The embodiment of the present invention provides a kind of method and apparatus of detection logistics package charging exception, it is intended to solve existing logistics
Whether rationally and accurate problem management equipment can not detect the logistics cost being calculated.
The embodiments of the present invention are implemented as follows, a method of detection logistics package charging is abnormal, the method includes
Following steps:
The history logistics data of logistics equipment is obtained, and fee properties and correlated characteristic are obtained according to history logistics data
Mapping table；
According to the correlated characteristic data selection criteria feature of fee properties and the mapping table of correlated characteristic；
According to the standard feature and to the data in the mapping table of fee properties and correlated characteristic, history object is established
The characteristic statistics model of flow data, the characteristic statistics model is for indicating standard feature, fee properties classification and each expense
The three of the corresponding threshold value of feature classification is associated with；
The standard feature and fee properties of current package are obtained, and is united according to the standard feature of current package and the feature
Model is counted, the corresponding threshold value of the affiliated fee properties classification of current package is obtained；
According to the fee properties of current package and the corresponding threshold value of the affiliated fee properties classification of current package, determine current
The fee properties of package are with the presence or absence of abnormal.
The method that the present invention detects logistics package charging exception, by establishing the characteristic statistics model of history logistics data,
Further according to the standard feature and fee properties of current package, and according to the standard feature of current package and the characteristic statistics mould
Type obtains the corresponding threshold value of the affiliated fee properties classification of current package；According to the fee properties and current package of current package
The corresponding threshold value of affiliated fee properties classification can determine that the fee properties of current package with the presence or absence of abnormal, realize detection
Logistics cost whether rationally and accurate function.
The embodiment of the present invention also provide it is a kind of detection logistics package charging exception equipment, the equipment include memory,
Processor and it is stored in the computer program that can be run on the memory and on the processor, the processor executes institute
Program is stated, for realizing above-mentioned method.
Detailed description of the invention
Fig. 1 is a kind of flow chart of embodiment of method of present invention detection logistics package charging exception.
Fig. 2 is a kind of case figure of embodiment of fee properties of the present invention.
Fig. 3 is a kind of case figure of embodiment of regular fee feature of the present invention.
Fig. 4 is the flow chart of the method another kind embodiment of present invention detection logistics package charging exception.
Fig. 5 is a kind of schematic diagram of embodiment of fee properties of the present invention.
Fig. 6 is a kind of structural schematic diagram of embodiment of equipment of present invention detection logistics package charging exception.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The present invention provides a kind of method of the detection logistics package charging exception of embodiment, as shown in Figure 1, the method packet
Include following steps:
Step 101, the history logistics data of logistics equipment is obtained, and fee properties and phase are obtained according to history logistics data
Close the mapping table of feature；
Step 102, from selection criteria feature in the correlated characteristic data of fee properties and the mapping table of correlated characteristic；
Step 103, it according to the standard feature and to the data in the mapping table of fee properties and correlated characteristic, builds
The characteristic statistics model of vertical history logistics data, the characteristic statistics model for indicate standard feature, fee properties classification and
Three's association of the corresponding threshold value of each fee properties classification；
Step 104, the standard feature and fee properties of current package are obtained, and according to the standard feature of current package and institute
Characteristic statistics model is stated, the corresponding threshold value of the affiliated fee properties classification of current package is obtained；
Step 105, according to the fee properties of current package and the corresponding threshold value of the affiliated fee properties classification of current package,
Determine the fee properties of current package with the presence or absence of abnormal.
In a step 101, by collecting the history logistics data of logistics equipment, the history logistics data of the logistics equipment
Including departure place, size, package weight, logistics mode, duration etc. are wrapped up in destination.Expense is obtained according to history logistics data
The mapping table of feature and correlated characteristic, correlated characteristic are other logistics characters other than fee properties, such as distance,
The mapping table of departure place, destination, package size and package weight etc., fee properties and correlated characteristic is as shown in table 1.
Table 1
| Fee properties | Distance | Departure place | Destination | Wrap up size | Package weight | 。。。 |
| 5 yuan | 100 kilometers | Xi'an | Weinan | 10*10 | 1.5 kilogram | 。。。 |
| 3 yuan | 20 kilometers | Xi'an | Xi'an | 5*5 | 1 kilogram | 。。。 |
| 。。。 |
In specific implementation, after step 101, the method also includes following steps:
Step 1011, the data in the mapping table of fee properties and correlated characteristic are cleaned, obtains expense spy
It seeks peace the correct mapping table of correlated characteristic.
Specifically, cleaned by column to the data in the mapping table of fee properties and correlated characteristic, when cleaning, can
Data of the data other than expense mean value ± N* variance are washed as outlier in a manner of using ROI or case figure.
More specifically, data cleansing done by column to all data, when cleaning, can obtain the value range of normal data by case figure,
Point other than the range is got rid of.By taking price as an example, the case figure of fee properties is drawn, as shown in Fig. 2, if being 500 by value
Point be then that abnormal point needs to remove, can be obtained by the case figure of regular fee feature value range after removing, as shown in Figure 3.
In specific implementation, as shown in figure 4, the step 102, specifically:
Step 1021, from selection criteria in the correlated characteristic data of fee properties and the correct mapping table of correlated characteristic
Feature；
The step 103, specifically:
Step 1031, described according to the standard feature and in the correct mapping table of fee properties and correlated characteristic
Data, establish the characteristic statistics model of history logistics data.
In step 1031, the characteristic statistics model is for indicating standard feature, fee properties classification and each expense
The three of the corresponding threshold value of feature classification is associated with.That is, the standard feature of package is obtained, according to the standard feature of package
With the characteristic statistics model, the fee properties classification of available package, fee properties classification further according to package and described
Characteristic statistics model, threshold value corresponding to the fee properties classification of available package, passes through the fee properties and packet of package
The comparison of threshold value corresponding to the fee properties classification wrapped up in, when the cost value of package is right not in the fee properties classification institute of package
When in the threshold value answered, it is abnormal can to judge that the expense of package exists.
In step 1021, feature selecting is carried out to fee properties and correlated characteristic, removes feature ordering last M%
Feature obtains.Specifically, the related coefficient of fee properties and different correlated characteristics is calculated by the ReliefF algorithm of R language,
Specific relationship is as shown in table 2.For example, removing the last 2 features, it can according to related coefficient from correlated characteristic
Distance and package size are selected as standard feature.
Table 2
| Correlated characteristic title | Correlation |
| Distance | 0.7 |
| Wrap up size | 0.65 |
| Destination | 0.4 |
| Departure place | 0 |
In specific implementation, the step 1031 is further comprising the steps of:
Gather according to the standard feature and to the data in the correct mapping table of fee properties and correlated characteristic
Class, obtains the mapping table of the standard feature and fee properties classification and the expense counted under each fee properties classification is equal
Value and expense variance；
According to the expense mean value and expense variance under each fee properties classification, it is corresponding to obtain each fee properties classification
Threshold value；
According to standard feature door corresponding with the mapping table of fee properties classification and each fee properties classification
Limit value establishes the characteristic statistics model of history logistics data.
Specifically, the clustering algorithm specifically: one of clustering algorithm of PAM and K-Means.
That is, clustering using clustering algorithms such as PAM or K-Means to correlated characteristic, and count different
Expense mean value and expense variance under classification.For example, distance and package size, the two standard features are clustered, wherein assuming
Clustering number is 4, and by taking each fee properties classification is classification 3 as an example, the expense mean value and expense variance of classification 3 are respectively as follows: expense
With mean value=10.16, expense variance=7.
In specific implementation, according to the expense mean value and expense variance under each fee properties classification, each expense is obtained
The corresponding threshold value of feature classification, specifically: using the expense mean value ± N* expense variance of each fee properties classification as each
The corresponding threshold value of fee properties classification.Assuming that fee properties are normal value within the scope of expense mean value ± 0.75* variance, then scheme
The point expense in red circle circle in 5 exist it is abnormal, i.e., expense within section (10.16-0.75*7,10.16+0.75*7) then
For normal value.
In specific implementation, in step 104 and step 105, for current package, obtain current package standard feature and
Fee properties obtain the affiliated fee properties class of current package according to the standard feature of current package and the characteristic statistics model
Not, i.e., the affiliated fee properties classification of current package is calculated according to KNN, it then can according to the characteristic statistics model and current package
To obtain the corresponding threshold value of the affiliated fee properties classification of current package, according to the fee properties of current package and current package institute
Belong to the corresponding threshold value of fee properties classification, determines that the fee properties of current package judge whether that price is closed with the presence or absence of abnormal
Reason.For example the distance of some sample point is 150 kilometers, size 10*10, then should belong to classification 3, the normal value of expense
Range (10.16-0.75*7,10.16+0.75*7).
In specific implementation, the present invention also provides a kind of equipment of the detection logistics of embodiment package charging exception, such as Fig. 6
Shown, the equipment includes memory 601, processor 602 and is stored on the memory and can transport on the processor
Capable computer program, the processor executes described program, for realizing such as above-mentioned method.
In specific implementation, it the present invention also provides a kind of embodiment non-transitorycomputer readable storage medium, deposits thereon
Computer program is contained, which is executed by processor, for realizing such as above-mentioned method.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution unit or equipment (such as computer based equipment, including the equipment of processor or other can be held from instruction
Row unit or equipment instruction fetch and the equipment executed instruction) it uses, or combine these instruction execution units or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution unit or equipment or the dress used in conjunction with these instruction execution units or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can then be edited, be interpreted or when necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, multiple steps or method can be with storages in memory and by the software of suitable instruction execution equipment execution
Or firmware is realized.Such as, if realized with hardware in another embodiment, following skill well known in the art can be used
Any one of art or their combination are realized: have for data-signal is realized the logic gates of logic function from
Logic circuit is dissipated, the specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene can compile
Journey gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing module
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
The embodiment of the present invention is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as to limit of the invention
System, those skilled in the art can be changed above-described embodiment, modify, replace and become within the scope of the invention
Type.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (7)
1. a kind of method of detection logistics package charging exception, it is characterised in that: the described method comprises the following steps:
The history logistics data of logistics equipment is obtained, and the correspondence of fee properties and correlated characteristic is obtained according to history logistics data
Relation table；
According to the correlated characteristic data selection criteria feature of fee properties and the mapping table of correlated characteristic；
According to the standard feature and to the data in the mapping table of fee properties and correlated characteristic, history logistics number is established
According to characteristic statistics model, the characteristic statistics model is for indicating standard feature, fee properties classification and each fee properties
The three of the corresponding threshold value of classification is associated with；
The standard feature and fee properties of current package are obtained, and according to the standard feature of current package and the characteristic statistics mould
Type obtains the corresponding threshold value of the affiliated fee properties classification of current package；
According to the fee properties of current package and the corresponding threshold value of the affiliated fee properties classification of current package, current package is determined
Fee properties with the presence or absence of abnormal.
2. the method as described in claim 1, which is characterized in that the method also includes following steps: to fee properties and phase
The data closed in the mapping table of feature are cleaned, and the correct mapping table of fee properties and correlated characteristic is obtained；
It is described from the correlated characteristic data of fee properties and the mapping table of correlated characteristic the step of selection criteria feature, tool
Body are as follows:
From selection criteria feature in the correlated characteristic data of fee properties and the correct mapping table of correlated characteristic；
It is described according to the standard feature and to the data in the mapping table of fee properties and correlated characteristic, establish history object
The step of characteristic statistics model of flow data, specifically:
It is described according to the standard feature and to the data in the correct mapping table of fee properties and correlated characteristic, foundation is gone through
The characteristic statistics model of history logistics data.
3. method according to claim 2, it is characterised in that: the correct corresponding pass according to fee properties and correlated characteristic
The step of being the correlated characteristic data selection criteria feature of table, comprising the following steps:
Correlation calculations are carried out to the correlated characteristic data in the correct mapping table of fee properties and correlated characteristic, are obtained every
The related coefficient of a correlated characteristic.
According to the related coefficient of each correlated characteristic, the selection standard feature from correlated characteristic.
4. method according to claim 2, it is characterised in that: it is described according to the standard feature to fee properties and related
Data in the correct mapping table of feature establish the characteristic statistics model of history logistics data, comprising the following steps:
It clusters, obtains according to the standard feature and to the data in the correct mapping table of fee properties and correlated characteristic
To the standard feature and fee properties classification mapping table and count the expense mean value under each fee properties classification and
Expense variance；
According to the expense mean value and expense variance under each fee properties classification, the corresponding thresholding of each fee properties classification is obtained
Value；
According to standard feature threshold value corresponding with the mapping table of fee properties classification and each fee properties classification,
Establish the characteristic statistics model of history logistics data.
5. method as claimed in claim 4, it is characterised in that: the clustering algorithm specifically: PAM and K-Means are wherein
A kind of clustering algorithm.
6. method as claimed in claim 4, which is characterized in that according to the expense mean value and expense under each fee properties classification
Variance obtains the corresponding threshold value of each fee properties classification, specifically:
Using the expense mean value ± N* expense variance of each fee properties classification as the corresponding threshold value of each fee properties classification,
N=0.75.
7. a kind of equipment of detection logistics package charging exception, it is characterised in that: the equipment includes memory, processor and deposits
Store up the computer program that can be run on the memory and on the processor, which is characterized in that the processor executes
Described program, for realizing the method as described in claim 1-6.
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